{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "LSTM_sine_wave_basic.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Alro10/deep-learning-time-series/blob/master/LSTM_sine_wave_basic.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YxOUeAZAQvxg",
        "colab_type": "text"
      },
      "source": [
        "# LSTM sine wave prediction\n",
        "\n",
        "References:\n",
        "\n",
        "- https://towardsdatascience.com/using-lstms-to-forecast-time-series-4ab688386b1f"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z0bRzPkZR1vn",
        "colab_type": "code",
        "outputId": "bbbe1f47-afca-441f-a5ae-a1e26d9916db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "# Import packages\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "%tensorflow_version 1.x\n",
        "from keras.layers.core import Dense, Activation, Dropout\n",
        "from keras.layers.recurrent import LSTM\n",
        "from keras.models import Sequential\n",
        "import datetime\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nvQasdi-Xm_j",
        "colab_type": "code",
        "outputId": "2ec56aa7-641d-414e-ef00-71f3aafb51b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "# Take dataset from repository\n",
        "!wget https://raw.githubusercontent.com/kmsravindra/ML-AI-experiments/master/AI/LSTM-time_series/sine-wave.csv"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-11-29 14:03:59--  https://raw.githubusercontent.com/kmsravindra/ML-AI-experiments/master/AI/LSTM-time_series/sine-wave.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 66713 (65K) [text/plain]\n",
            "Saving to: ‘sine-wave.csv.1’\n",
            "\n",
            "\rsine-wave.csv.1       0%[                    ]       0  --.-KB/s               \rsine-wave.csv.1     100%[===================>]  65.15K  --.-KB/s    in 0.01s   \n",
            "\n",
            "2019-11-29 14:03:59 (4.39 MB/s) - ‘sine-wave.csv.1’ saved [66713/66713]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ze70qu10RNVh",
        "colab_type": "code",
        "outputId": "b49bbb11-906d-4f40-e37d-5c46489d2e40",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131
        }
      },
      "source": [
        "# Load dataset as pandas dataframe\n",
        "df = pd.read_csv('sine-wave.csv', header=None )\n",
        "print(df.head())\n",
        "print(df.shape)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "          0\n",
            "0  0.841471\n",
            "1  0.873736\n",
            "2  0.902554\n",
            "3  0.927809\n",
            "4  0.949402\n",
            "(5001, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "buGtEn8qULBf",
        "colab_type": "code",
        "outputId": "8e119f15-6a84-4a8b-db26-6bebfca4263e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        }
      },
      "source": [
        "plt.figure(figsize=(20,6))\n",
        "plt.plot(df.values)\n",
        "plt.show()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+cFlU7XD15UY7P2vQ92fjBgWhH3OF/em4gXirndByhb1Z+9zo5weggd0DynPT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\nXCnb8YaqX3ZMutOgBuYDcdkwEwHCqkPo3KTOkR2Yh3hdAM2aufBp4O4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yBir4bI4TIDFg\nC/C6aPjCgpVdkePlVatC6JlNiwov2BjCPqQk6GMts5vKbuLlJhCY8/nCuLwu0qAyxAPR/r27jRdH\nPHJ5G4T7TLTZbF2J87qkteXseilmPXAhLbgFW2YT+cLia5ZV8Lkv65ZePwogmbModimWxoh8JL2/\nuDMy6TFiNZfQmiXFosg+J7b6xB6XYSX97RxTY0Qg/AJiyh6v1iXKQFGwadEnJj9SEczxNZM5kITJ\n0XjbHqdwAmj4wvxzTOcLI3MgRdDB51Eu1mrvAZEFWCGYvIDjJFodT1N+a3hY8MP46xXpyl+s/YG+\nhWKm8iKMYrwuKU5oHQlSm8Qjp1olaYkr1v6EFFBfRpj7nMgXtkt7GMAjXATk7ZnhxCN3PCCd9yke\nvKTpXhyNw91n2zKx2/zCbYCbb8hLfqSjZ+L7LOF1iSM/dh+zCFU5Bbq3iLRXpCLN7PeJJblSHz/g\ntuXs0ibMvLhLx2PyhYUCfQnHSfUNQ7wuKbpcRC50EhLa4GMPoqLgLtxZxESANEaMFCbS4oe4rkjs\nA9vGMvc55EulCesgOjiRs8ja99CaU/1oNY9wXMxKPALpBahYITQ12VrE4mIRX9gu54Zzt6jGPF2+\nsPW6rO4rROBFdYfMHEhZTlHYyiqr7IYJF8VPMAaq2akIqdB4TSsSidfF/q7UNftI6STjNbD7KJko\np9IigTsvVn4CRztHyT772/bS+MLiHAbcKicg4QuLIwJElRtP8DIRnZsA2amIL4yre9FgIzE5unPw\nQtSV1OeMd0kESFpwfaSVEn6vXchOmaT9QyFbd63s2iSlT1ekrQYhvyInZPXss7C1KUR22v53u0io\nmi3jCwuTnVa6wuQ4kSUKqaS2kQvdZCTcZxpX324FstQW+FCCOZ0vLMLrImyZ9e3zRGATQ4kAKY9U\n8C4g5s7yn5sUvrDN4xH+dtTq93LQOHaOkjWHfKnkPhV/kIKMDk7yK36/Z8dM0b3oq4+JydGmIO9N\n+ucWtl3kYq32HpDU4CDG6yLJpO7CgSR7gpFjQGNBL5twEagcUaoBjWXgRQiDaM8upyo5a5wGp2oK\nDMHPkYZa2BWNk8YXxiYOrxILvuq4nC/Mt89SYs1wYiG5XSPYzkVOBCRe6HZtH029dAbXnNhGE3+Z\nhUx2KqjshvzKWJSQutsJpLSzvet4LF4XQOBLI34lleMklggQ8YXtUM2m+npBscj+nG88YPciXlmW\nWAT2eYgHLk6bLyzK6zKAfZDEI0HEYyJpf/zVR1ni8W49LgOkF7QWOySsU+YYRcqSH5cBavuQGCPa\nn3PPkdvCJuULYxVOYuNVc5Q9cBFLcu26L1EQgohTNrewZTllSW0p2TVIZT3TDdTP0pLhh8V692Bj\n98RC6uUmzBHAgt3bObI5ToDdId4xXpdp02pwFhIB4Utsuq5wW9jYfGFx+LTkchM7N2kJ5hji8VR1\nZYfn2FPmuCuvSzrPAldXgrwuYyEfEJMjLXApNsZUupL6UlWI12Wc1na8C9kpsLtfiRVO5HxhsXPD\nbfdMaysMt3uKWth2QiiSbSyzoDVJ8yuxNh9JUXAXG5u0z7G2vSbuTCvu3K0XUu2YLJ7MZjxy+2j1\n77jtnlR08CSV2sG2CXOKv4Ogg3d44CL9kSN/UZDPKZvewha6C0j5wmJx8Wm2sO36uEwy71N+hS3L\naUpqlTP+vHtd8UslJw0G5lx+jlkiPHnXS3EqSor7ElSkcpPYO74rf8/u1fGwI5dWv+LVqjSOEyB+\ncU/VFf7LC5FqVfI3jLRXJOqy/TnfeACXnHQsaSkhJlMWscA8kUx0E+gTK8UBstPqd6WT2k7HYV4X\nyZPV1AvdLtx65NdCB1nzjn4lVuUcBPEoRZIEWs5S+MKGuNBViKGArqQSQO+EDuZdilPXHENxSRDW\n8Utx4mt7zRw5NjGKlJW0sO3SisSMERNjuvia0+KRKK+L9AXEu/zARfXvdqUliHMqpYxn5xhDKHJf\nXJV2YERa2E7Tl+4S01GR9InnZsckHOvhg/MqOYF0xiTZ8dqgNxpscMhOgfQ2mhivS+rFPU52yod4\npxvQ8HhsjpPUfS7W4T3ZtCKltj7ykikb8mJOwmcXEkwgLdlaxPZZEGCFUFzJfGGRS3EqZDzWMguk\n73O8DTCVkHXHBDO5ypnUwhYhDp8IUBUx1OhytTtfWAwFMREFWGGS/dTkRxFN+kt5WDhte8059LaU\nCNqOI5eR1NdtilWJychg5EGupdqHIsItMQSSJNWXVkTDMQ4k3qW48aXJ7RURv5f6vDsRHbyrjd01\nTozHiMLkBzHxGEfCyXjm/Ocm8S4Q0b3RKJ0vLPbARcWBJEkEkBKPsThWeBeI+pWkR0K4MWIUES1I\ntha7JAoT7nxFDIRAfiQklS9sGblbiGlQAvt8HuVirfYekNQWkLgTSjegO/G6CJ7p9vK6pDrKHYMX\n7stz6TDTWMDGfLEjuXc8UrmpnqA31LacSSp6Jkp6naor3OoXEIetSs5NOGATVjkDL4C0/512PKCq\ndKZeiodABMT4wthtwskXOnLyI3QpnoxHKAXkpPFXkXYbz/K6cC8j8cQj08ZOE33pzq+FpraXB3RP\nUpiIoUaB3VuRdiU7Td3nGPIjvWWWmQCPXIrJiEcZXxj39T67f17y4sQ2mhjqSsqtF/OlqW00XGS+\nLf7GkimcNkBAUJiIJM1mk7QYMcp1lcgXtmsbYHocy9vnXdA9KXxhUQ6kRPuwa1GQ2u6ZyBe28z6n\ntrBFHpdh2tjzKBdrtfeASIk6fY58PDIwhve8OyB7Ccr3AgGQnjHf5Xl3YPfqlx0zhp6hOqFJKgw2\ndilOM3iblxy4l9h433NaoB/jdbH/bhfZlew0OagMvOiTigjYJdBP4QvbvFLCTcKFL3QC+0AO2Kqf\nI52bHclO0xLWA5CvR8ar5rhrawAXSdIgmiK6crqBvp0jS1d25HUhkpNOExGKi6juJVZ2d7wUp+5z\nzD6kkhdHbSwx8ZiahIuNJ+MLi72Qml6YmI3DLbP23+02Xlj3Gh6pU2xhW6zW3vlV43ET1qktbFZH\n2b40XiATxLEsXYkmrAXo4B0SzElxbBFG96TzhXF9c3OHpBe0+DY29OojsDttyc5FwUTKjZDunUfJ\nCaQzJqn9qzFelwpJkg4zjQbmSUFvjO8j9eWFXaucXIPHruwOcylOTQRwyYvvdmtT9e/SAqwYN04q\nSop5bmKJzFQuiF0vsancWcx9juqKlFsikghgt7Cl8yzEOJDSyE5jHG4AkyMt1WaHEQb275J0ZQfd\no/K6JLawxc6NjC8sxjMnSwT4RIqeYV1uYrwugKygFbsUS7iz4u1cHHRw9XcCvjBqIiAeI1b/LvVC\nx2thi6IgBIWOXYo761NOBLB1hRojrtYYDVEUvKt8Yel+JaZ7KXOM6YqcUzbGvclFjab6PTsPl6Qm\nW6MgBDFf2MVKqVys1d4DsoHOcZwQIHAa5OfYF6t4OxeQXmnxXooTlX83CGdaEi6W/JiMRlgntJTE\nkB+picddzk0yWeAuQSq5zcf+u93GC6O4ZISLYV6XVL6wXQgXq9+benHn8IXtdm4SEQE78YWlV9t9\nvC6DES4SSSvZT0w3QSXJPmwqfsxLMbtNOLVdY9eq5K7ItXhgntqSuls7BBEJNxom2bqrL43xugBS\nvrA4ajS1pSTGw5L8MAox2bqLL6USDScmW+MvXabpXvXK7G77vKvs8rgMgJ3b9mKvPqZSO8Qej7C/\ni4p4lMSI/z97bxcrW5ddhY1VVadu2+kEbNxyWjbtGNFSaETUlr74BSmRcKOYF7cfSGKkKB1k1C/h\nCSXCyBIPKJZM8uC8ECktSNKBSIY4imgpjhDY5okY+KQ0GDdy3DQPuGNwyyEE2933nKraedi1quo7\nd681x5h77PNT5yyp1fe7t846a9fac8255hxjTKbQIfupQANJbDwS61D6kv6nO5+sF2ZKPAY6uuOc\n3nhER40G+yzLoPQLqxm9sOjdvsbxsp72GQyZO04E5nrCp38pHuHTzoRUMgMfVIpljZOgJbTkhCJd\nFxWeHATmsgYSmQhQ+e2RDosbCQf4tGxU7jij63Kj2kpwKV5KL8x5Kc7Ak3u6LnriMQrYqgaSp1pV\nSi7YsF/ogiTc+DlPVfJke6KmUu9Ct5F1ERi6p45Aauu6ZP1UVNzxJT82qURAn2oN8GdsdCnWKXGx\nrosbHZzXC/MUJhh0sKwXFnR9tKODVxodnO2AyJ4P+8OAISgKbla6n+KKOxpNuFkU3IjznQpkvfNh\nAUq9Wky2FgX73+EpphPpnnHSX5XH6KNGxzWKZ6JJL4xDlWu+lEFxZRJSMWXWB7zQCxOvGkipUUr5\n/lLKL5VSvlxK+ZGJf/8TpZQvlVL+finlZ0op33Xxb/tSyheP//uCYz3Pechw56ALQZ3T2dFHrWLE\nEHTNUcbipDkdqeiZnYKLungxx/VmHWUkdlr/Tevk0IetbkUhPrpaJScCPHph5y5xERXJGLAlKWzN\n90aez1+tiro+3myKlIS7JekVfDetGrAFSTMpSHXTcji6pyswlzsgEkiS7Ua7jDAJZkmc9DBQui4u\nDSRAR5JEXR8zAtCRDwCM6OBT4lGbz611xTwza89sd0/23eZQ5ZpeWNRNa4kYEeBRFVHXx6ztRSgp\nt8zBuEYXOlizvagzHlCLv5quiztGZOhcdr0woSgY3VcyyY+IBlg/x4zIl6p6YTSqXEWuBTGiM3mb\n1Qtz+pXozneNY3YCqZSyBvDnAPwhAJ8A8EdKKZ+497H/E8B7wzD8WwB+CsB/efFvXx+G4ZPH//3A\n3PU89yFnUoOArf6bE36YobBRfHkT/FClsLEZeKcQ3xnt4tEIyFLYuh197PBkDUnCiJ0CiWfuBBvK\nPtNVDKOYu64XFlzoRNQVayvW5EeCwsZRZp0BVqFh9/vDgH3Y2jcBnw5g9/Vz1HwBTL6iRh+TwhYn\nP3REQM7UXyYAACAASURBVITSqL+Xmo85HzIUthDR5EPCbbIUNltnqRjFlaEqOgsTd/vDSc9qaqgU\nNuq8WUAvzEpRSeuFtYs7qyIgZckknOqnmKSZa59VZD7zzDqaN+j6mIoRfaL9TCF0I7AHqPjGrJ2V\np7CZdaSMCEXGVjS9sFjmYPyc+MwP6EuvcTie9nsBfHkYhq8Mw3AL4CcBfPryA8Mw/NwwDL91/M+f\nB/Cdht97lUOvFBOVlsRlJFLkV0UmrVXO4+daivcrUZyUzcBropUD2XmOX+Nm1YFwmilxwPEAFXVd\nuh1AxA5BSyUCeu+24jT4S7G/yumqtGSTcP2OPqpeWPzMNenCrrFne0vohSm6CNH5VX+XrhfGJOm1\nCn5rjSoth9J1WSBhXT9HzxcElMp8t+Q+s89cdV16nex0JEncprt+jpuvng995BqbeKQvxcZLbEYv\nrJ/0F21l17c9ILfPbo2TfoyY1QvzxJ1RQqrOZ31v5DPxuM9Nymz5wOfi9TGIx8Q+B77Uing0Jx7H\nf9N9c7TPanMZptOli3LmRhvX36W/N069MLevP36H3a6KPNOG0dG9xuF42u8A8E8u/vtXjn/XGj8M\n4H+/+O8PlVLeL6X8fCnlBw3redYjm0yJMtwKhDMOKjMaJ/0KAeBrwTj+Gw+R5HRdEhonjOCicOAt\nkoEnqgTsCGHtR40TXpyUrFbJemEedB1dxTDS9jJ6YaV0up5kO3aE8GRj8kNOjvbFTk8aSCaxU6AK\nfZsDNlm3gUE8es6HakMqXSPu6KMUJiJdFw3KH4q5J5O3XXSw4EspXRdVqy88Y+t3qNlKD+WpoK54\nDaTH1QtbokDWR4a5kR8qoomkAarJj7AJhxMdrMWxEYpLFWyue9IqCm5F2+MQjyptL95nRS8sRKaI\ntkcXf02UOMDf+OC0z7QGUuSbxSQcWQiVKWdBjDh+jkcHd6UikoUOl14Y895c49g85C8rpfxHAN4D\n8O9e/PV3DcPw1VLK7wLws6WUXxiG4R9N/OxnAXwWAD72sY89yHofYyzVglFv++2FMzoP0KWqVVHw\noiZTmGfmq5Le6tcy7di5oHJ/6FdkzvORgblYzXZdRuhLsdSFrX8p1vXCxj1p67qoSbgFqlVBYH4+\nH7wXOrqyyzzzhk9YR12bxt+li9p+89aLeBzXOD1n1QtzozytoraJanb/vU6K2prOWAZtrCasGWoT\n4KOw1TldGifA09ALY94bqwaSoBd2OLC6LqNeWMtX3F+jU9eFLgoam8ss0WUW0PS9HhJtDIy2IiXN\n9oHI/sUa16s1tcb+mZ20le6ZyCfVqcJqBklPxbH8mbjqFQXlwglRFNys8PWv31HzAaNfYZ5Z2WfG\n9uwMDLmRwqsGkjq+CuB3Xvz3dx7/7gOjlPIpAD8K4AeGYXhb/34Yhq8e//8rAP4mgO+Z+iXDMHxu\nGIb3hmF47yMf+Yhh2U9zqJViplqlXEbYKobCX31oDaQ6pxyYRwkpsQsbVa1SgtTuAZ8TketWOYVn\nZnRddLQLqYFkrFZJFDaqyultBZpxlFTFjw5eltAL6wtrns5EWm+n/8xpClsQSOuX7L7tqV0fnQnr\n8ULQ1nUppeBG6ATFXhCtdIiEL+WS9Or50KlyCsg1ns7lpAnrleJ4jbxeGIMOzmgy9tDBul4Yh1zz\n2opQ6CAE7E9JLiGxx8z3WFQk5kKXSTxSGkhCIoCLEZ2JAHe3UH2fmYQU3xiFQZXrdwG/pmy8z0oz\nmN58ql4YZXtyl1kOHazQ9pzo4Fu2ECrEnEAfhHCNw/G0fxfAx0sp311K2QL4IQAf6KZWSvkeAP8t\nxuTRr138/beUUt4c//xtAH4/gC8Z1vRsh7tSXP+NnS/qKAJcBFgC5JKqcrIdO6hggxcv5ip+4gFK\nVPwAQYTWnFg4dXIIOsXJ76ExONjRiADx3TZpQfDJFOW98be57dmJrBfGdn0k3+thGGhdBP7djhKZ\nC+mF2c9sNRHgS9LvggBwnFOge5IwecVWdqGuS8ZWfIhHap83ehIuQn4oemEMMgUQnvnQ13UBNL2w\nU3eukNqkvDesLxUSAQt0XO3bM4+eYQtuyhqjboBZvbC4iMe+1+QFUUY8MjQa3lb6CXA1RvQWVhnK\nbAYF7ix07PbMPvMi+6cEeGB7yl0g6jwnnw+7fiJzXCPvS3fm9waIuz5mClpcwpr3e+PPeZKtzF3g\nGsfspx2GYQfgjwP4awD+IYC/MgzDL5ZS/kwppXZV+68AfBjA/1xK+WIppSaYfg+A90spfw/AzwH4\n8WEYXnQCSTYsQnBR6QTFapwAWptIN2S8p+syzslD+RmxU6VSXNcYtXcHlIpfpIGkHaBnAcf+vqhU\nA2fC524fiHKng1RXtYqocqp6YWE1W9cL64mdAmLSjEQYsDD+kyMnBBelKmdgywAEeHIsuLhRglSq\nyllwd9D0wnq27K5yAqOP0Gk5DydqKyMed0PXVlREAKProrRjZxGPl5+Nxu2OEztV9Dm26zZlFsjp\nhYVipxK9nPOlil/pr0/Xxhl/znOhq7+X8qVCnBghKAEhRiQpZ8olO5ovgw7ung9q999A12W1GvXC\nVG2cKI51I+EAEblG+KnHKqBzZ2xGODz2U9J9KkC6jEl1bZ+7ovhCHMvo6Gb1wlojrxcW2Ypoex1f\neo3DooE0DMNPA/jpe3/3py/+/KnGz/0tAL/PsYZrGdlLcZQ9/o3djpqPrXIqa6TFTgVR25sgSFU6\nJbC6LloiwIsYipzQ2IJeuIyQz6xWv6hgQ0hy9XRdVL0w+jLyiLQctprN64Vx1SrnpTj1HRIVOkUU\nn6p+mSls7qC3ipNyemGkzpywL1yV02sr7PoYXZdt4jJC+T2T2Gn9N+elWC9McNoSil5YiFxT9MIo\nXRcz9TGBAo8oJaXwCWv2vVFsOZwvgSqPdDIBLenf03UZ51wJMSLnV6T3htR10ZBrTt9MIGU3fIzI\ntncHNJTUh26YGNEXdyqIR4UmrOmFuZGyMTpYtpVgn2XEo1MvjJaz4AsnQHw+qDpSrxS21/GoYwlx\nUr+uSwYRQAQvQsY8utwo3Wio5MdGFVwMdF3UDHxwKQaSl9gHvhRffjacM9KRSmicbFYFqx694kno\nhT10sOHfZzqgJCvFl7+bWeMSemGuRACn6yKiZ/bmro8BEg6ApItgT6YQui4yIoC1PZka3d9nHjUa\nB7062iXSdUlUiolLsYKEA/rPrOiFjdVxDh2sJNV7frTqhfGXWC8ahyoypvTCiCS9aT6g+hWf7ekU\ntqDBRaZbKBHHqnphNm1QQtclF4/4EtaUL1350cHAEuwBpZjsvwtEWplOZP5pnxXEY8CyGX+3b581\nClscj1zjeFlP+0xGhr8aawQ4hYZFYw11XRJCfM5LMaHroiThKF0XMQm3O/T1PuqcfvSMM2DT97l7\nKZYrxcSlWEoELKMX5hTqjLjo45z6ZSQKpFXbY5AfLsFFOehldF3MqCvVVmJdl8QFsWN7AI7daNSq\nZH+frXofqnZW8N6sRb0wppqtdAjaMbaSQs84acLxGasVtFh9Ds72JF0Xa/JDKGix6GA5RvRR2Ogz\n1pTIBNTzhvsO90JxJ/KlOVS5z1YoXRfJT/GocinZak88li4SSLOVhy+EqqL9O8ZWUsnWKJniRcJd\nfpaZs7s+WS/MW9BitIOvcbysp30mIwOdc3UpYcVOAQFmGlSzb8yV4rpGGjJOIAw2qxUOR0pJNKoj\npzSQTDpSgAYz5aqcisYJD1v1VzkValP/UqzohZ2C1EBwEVD22dti+pZBkggBFhOYV9SVMp9zn6NL\n8Rm5piEeu7ouCfH1bpVzI56x5IWOrmaTZ6yVRpMROyUS1gqKK0LPbBTUFelLra3JayDtRl0J9kyh\ng52XEUEv7HQpJtDBru+w/r4njQ7eaO9NRBOu36GLSgn4EY8n1AJhf5SuSyKO7cWIQPaMdVGRuDP7\n8rPMnP3OvxltnCAJJ2j1sTqZ4xoVv0JIeJjvArKfCmUJfIWTnK2058vohUWUWUUvjHlvrnG8JpCe\n4NA6QR1f3I6uy3bNC0pzl+JEIoBAkvAaJ2SVU61mm2gvbEafnQ/gkh9awoeo0CU6BHGJR3OVU0I0\nMYlHbxWDXWPtnkR9h0oHxOCZlYQ1TecyUtjUhHWU/Kh6YVoFkUE8+m1FSn5Q741bA8lrK6pgrLW7\nDXGhU5FmFbXUGsp7Q+lzbLSE9RJ6YctQZvvfYdULiwaHCtP9FKUzZ9VA4nWkWCQcIIqlGzuusu+N\nEiNermNqKIUJShB/oxcm4sSjUvxlkK3eZIob8Xgq7pjoYUASdRU0PhjX6EKVJxLWETpY8KUUqlw6\nsznU6OVnwznJQqgrXgI0vTAGNXqN42U97TMZaieoSNdlvNB5gxdA5a96D9AoEaAhAhQUBJFAYnRd\nzJXi+vusFXyFrkF25wJ8XO+MXpiXXsFUOfl9pirPKb0wAnUlBgfdIFXQC+M0ThLnA2Ur3iSckwao\n6Hsxui56pZgJ2BQ6F0+vYJAkVJXTjHgEdCQJg3h0w+4vPxvP2adGZ/THOD/l8yuKXti5cGIukEXJ\njxVPYTt3SHUhkOIL3QkRIPgV6j00xYhALWg5C6E82oVNZF5+NpyT0plTntlL96SKjCve9gCEdK5S\niuxLo+9QQ40SzywWBQ9myixDE75Z6xS2CBkm3y16zWAyFDbizqdob3pjxDguvsbxsp72mQytykkc\nJkoVQ0GSuDLwKQobUeUUhIvHn2GCA+FyY+wsxVU5NXhyYbqeGCvFmWo2A/G22oqQbGUuxafKrnS5\nMVernBc6cp9VCht3oXusapU3mcKguBRdBErXpVJUzEgSFbnG0PYkRABhK97zQfOlTMJaRV09JPJD\nRa7R6GBV7JQRDie+xyVoORGKC9ATjxFldrwgGhOPIoUtspWKvFO6LEXxjbvBhdJ5bonkLUOZzVHY\n+muk95hCB9f4Rkh+hM+s6YWFMWKGPmpKji6CzF/gvGHWyOqFLWYrBDrYicx3o8qvcbysp30mQwpS\nGV2XRBXDWmkJgo2VKE46oq6YRIAYmAcZ+MvPMvPd9JIziQx81M5beeZ6Ke4GqRuv4KLaWYpxlGq1\nKvoOJTSOuVpVv5du5VlMttLVKtVRduxvs1qd6Hj0fAz6T7Dn2Fa08ybWddEpsy4KG4UKEyvFt7vD\nyV7ba0zoaXSSHwrqqn6mt88qhY1JBGi+1Iwarc/csT0laXY4DNiFlNmKNvairqzo4A1/PtRLX+/d\nPiXhzJ3nFL/HnV9L6IUZfelKOB9I21Pfm+4+C2cih3isxR0BxdXx9YBW/L3bx7oumYYZvX3WE4+k\nL5XiYl/CmnlmBRHNFaczKE8C8Sjf+Yh4hEjQ3BJ+yq2BVNeoICh7cfY4XwJJ/6qB9Doee6gHaETn\nGkXkjILSAoWN0XUZf5/C6SerGEZEgJYIIL5DORFAito6NU6OASAnTipUdoWqJANbVfY5rnImNJB6\nl+LMe9Olhy1QrVoLXO/9qOvSpcya9cLOgqxeW3ksxCOD1lOSKRxqVKsU0wHbApVd5t3WqE2KLw2q\nnJIv7bf9HtcoUJvMemHn7qPt+UopGgVkoaRZ31YyiQAf6opBzyzxHcoaJ0SDC+bdPlNmCV+qUOKI\nro9KjAgE+1z9FEVhIxofCLY3zulNWLN+j9ULW4TCZvelxBmbSKZQ+8wg14S7hbswoejgjT/DJLkU\nvTCGwuZDB0vNoujv0AdCuMbxsp72mQyVv2qlVyiIAOlSHDsNpVrFUZvEDDwlyCokU4j5lGoVQ2Fz\nX4oBLpCWaIBEwDYMw0KJAG8Vo/5Ma2yFZOsSOlLMpVi1FQZKXH93NJRLsWQrZnhyjIQz04SFyi5D\nA9T1wuJ91qiPBLou9cxEwvoRfSmDoPTquijfYXxZqnO6n9lqKwl0MEd99PnSrUApYVGjrF6YJmob\nr5GhzAKQaDScr9cQTYARHUzouqSQ+UZtPVbXhV0jV9Dya6RtlaQ68948KoVNQPcI0gnMM6s05ogy\nO67RUxSUbYVkIyhMG3eMWH/mJY2X9bTPZGzXGnecoTbtSP6qWwOJobwAunixs8p5hlx6goN6Ke7q\nupy65fDBAUVFkmg+sRMa10g4SknXJZ6vJq0iiLeuF+aDjJ+qnF1KiW4rbr0whu5JtyulEE3Ce0Mk\nHnUKG5P8UGjCA0HnSlBmTRppDF1j7DynURWdqFEmSN1KiQBGf+z43ii+lKFGC5di5kKn6oXZqI+7\n+t746FJ+dDCxz4JeGPUdJvTCqPNBKhbFCanxs8r5ECMMlMQjdT4omiTmGDFao0YTrjGiZz6A7Ky7\n0c5YxpYBsamHKREwItcIX6rqhVFJOB/iMVPo6M23XhWsik9TFtD1whi/B3C+1M3oADgNJAV1xXbW\nVfXCIvu7tvGaQHqCQw1S2QsdY/zVAJnKDYOqYDSVgArlJ42foMRtRe74uMb2nGfaHvEdEtQAvVrF\nte5Uql9stUqqZvc6+gi0nPoeWi8jhK0okPGa/HNVJU9Js84adb0wVjicfOYDo2HgpbApVc4DTZlV\nRW2j84sPUnfEBUyrjpMXOpVGQ1To3Bon9XdHY0cE5krVFKiitvE+s0m4HWl7Tr0wqbMU4acADT2z\nY21FOG9iXRclEbCMXpizwcWOsL2MXpgNVX6IbW9co4KkHwjb4xOZjC9VkGYKEo5NPDK+dAmENcAi\nSeLYXUPmx4VVoFLOnGdsoptw93xIIB6dvpRIpmh6YQqqnNhnwvZUvbBIqw/Qir+7gxdtXPfulcL2\nOh59SMkUMngBREQA0/7UBIMd5xS0ZxYQXKxdQ1rjzHv2BPo1AKSRHywiQKhmMyiN+llmvvFnPJdi\nJmADHlkvTEo8KhS2eF94LQiGwiZUikkkXP3d4XyUrdQEOBO8cO/NIhS2g6oX9nBCnQCOGiLeBLP1\nciOgKhhdl3Mi00cT3op6YcyluH42GkzXRw3xyFHYVF9K2Z7QITX29Rk/Fcc3iq1w1XFv0n9cI+9X\n+k1CdLQx40v9qHIxEcAkmKUCWXu+qhemsAe4gpYzRtTjWHtzGec+M8kUsbAKBAV0BZlPo/UE6QSy\nC5sSx0a2fNpnqlFB/N4oemGsjq6mF8adsape2CuF7XU8+lCy0VRmVnKU3kQAa1gqzJTTdVGqGJwe\nkEJh6z3zSCkRMubkhU7rlrPEe9MTztMh44zWlVTltFYdYl2XnF6Yr1q1owNzn9hpqirZE1xMVE2d\nemEskoQNNpjkqPLM9cJCnQ9WsVMzajRBYaOQsmSQSum6yJeb2O8BGuqKe2aBdhyKFwsdvwhbkegV\njK6LWTtL1QvjqtkapZ5NtjLPrKBx3BQ2J1JWlzngdF2o71BKfvgSAQqSfkfpuvAoKfddgEHeAksl\nHjm9MGafl7j/SLZy4GjCblHu+llmPiACIXjnA1S9MA79x66RZUxc23hZT/tMxnbNd9jgAn3eUVJV\nzkQGnmvB6KxmuxX5lY4dtULnrEp6xQJvBSSJa5+XSTyKOgtGvbCKWGCCVCciQAukvdpZUpXTlbBW\nurrRKC4xERAFbEpyVOmmxbyHJIVtCZ05HrnGNT6ov5tZ3+XP9ObTtLjcSTjOr7jQwVqydQFEAKmR\nJvl6QuME8O2zohdWdV28yRRCAymF/DAXOsyFCQ7x6LQ9AcUlJAIkX2qPEVntLMaXEnRPoVjEoEbr\nnG7UKMCjrmLK7ALoYPHO9xgxYv1sNBQdXa2YHN/5JE1GApkPePf52sZrAukJDgXOyAVsPJSfMVa3\nEB8gtmNfoFoV67roVU5XgHU4DBzSTOCOM4G5e58luDOr62JGBJwcGykWSCdvKb0wwVbojh3eKidn\nK48n1KmguJyitmdKKrfGiDKb0ucgYOjuRICEeAzX50U8rgW9MD7Q5zsEUTRASQCa15bQntnsSwkd\nKXfb7/rZaDDoYAC0Xhij/Vd/nxMRoOiFUbouQuKRtRWVXs7oZO7p4g6X1Ku/m5lv/BlfPPIYSbON\noO/F7HMm8cj4UkW82ImeYalN7HwMnavOSSPpibhT0QtjaIBbpYhHFZMVJNwytkfvs1KMCcTIr228\nrKd9JkNLpnAtqwESEcBwxzPBC4MIsOoirHBg+auEE3JXOcd/5xzlSdfFXK1aBBFAdJZyVqskvTAq\nYNMQAWwSznuJ5fTCxuo4k/xQOkHxtsLtM3EpXvEJcKVS7Aw2thvhjBUuxRrVwNilhKRDOKlNSqHj\n7FciP0AmAkhdF7VSHO7Jii9M1GemuoWSl2yA0ECS9tmrF0b5+kTCOkQHk3phi9gecymW6ODxPtfv\n0KWNM/67QlX0FnekYpE5EcDs8/4w4EBRZvlkCpf8SAhAMwlrI+JxK2n1CYUJsojHUpskDSRrMxhz\nkxBzgwsW5cnqhdGFVUkvTGBgkGfiZlWwCrpGX9t4TSA9wbGVqlV+DaQSQTgzbXOZVptCNTtMfkgZ\ncwLOKMzHXIrrv1t1XTZuegXveJnkRyYhRWkgXVm1ikMEcEEqresi0UeNiUciMFf0whTKLI+e8UO8\nnfPREG8ZBWGmAdKJRx/yg/WlWpXTiA7e8EmzernpUWYfm8LG2govThqjuJZ6ZkpHaoGENUOZzVHY\nYgQSZSuLoINjXRdJW0+gzEr7TOmFPZLtMboumdidKu64n9mInhETzA+NzK//znyHwzDQvnQJEIL2\nzB6dWhaE4EbSqyipaE+ucby8J34GQ6sUe2k09VLc13VRgl42+eGtVimXEeVCxwVYQrWKQkgpiYUF\nqlUUnesYVHYgnOtVwYoUJ6WrVSoigEBxAbyjZAM2SUeKoV8p0N9Qv0eockqUWSXB7ElW0AHbhkdd\nsXSuy98frZFGwhkrdCwigKbMHs8vtvPcQ2sgARVJYtZAMtL2JHQw4+ufgl6YEz0j2J6rQyrA+1Ie\n8ahSm7y2Euu6JIpFD6xlo+pQ8tpZ3n22a+M8coOLCEmyXXPsAZo+KuuFseeD54xV7xbjGjzJlHrG\nUTGis/HBhkfmM5TZcY2cXtgZNcqg/3wFMkUv7JagFV7jeE0gPcGxmAC063JjhjMCaqtgpguBVsHn\nn9lYrWIpbPR3uFASjjzkGQjnhkYECIG5UQ9I1QtjNFPq72bmG38m2GcywFpO4yRaX8JWTFVJmjKr\n2Mqe6foo7LNAUWECLDYwZzVJTpTZMKjU9pl5rwFVL4zQLLIG+hrVgL3csCipkK6h0MvJRACLeOQp\nsyoigLM9JzqYPWOXQTwytuItkCl6YW7bq3MymkqXv78/H9H2O6GBxMSJEvL2wbWzBFuhE8ycLtw5\nEeCJEcc5BXQwkxwlfPOZEic8M+Hvnban6IUxKC4tYc2ficp8TuSaRtsj75AvrAMb8JpAepJj5HI+\nXgZ+Exwmq0XESfkg9XZ/wE2YqNCCA5ZqoOgibAJBNRZJUr/DaF/YantdI+PIx8+SHGAiA78lK2r8\npdirZaMjAnzJ1tM+B+/2DVnxOwcvsQYSrRe2H8L1KYnHeqGLn3kBaoCz6+PpvfHYSiZhzSS5tMoz\nlwjgLkyCzhypcTKuMQ4CXe3d6+/j9cIO1Hs9fpYL9GOf4kcHs2dspcxuon0W9MIkdDBzoRNQV5Qt\nn6rt8T4rOnPRdyjt8y5OZALHApQR8aiiruiCFnkpZvzo+FljoYPsBHV38ns+NA6FKhcpbJtV6bIR\nAKEwIRS0eL2w2JeqGmlskVFBPIb2TGv1se9NtRXPM2c0kBj7ewxNWWC0Ff6M9XyH1zhe3hM/g7FV\nEgG7Ib6MnAIsT6V4nJN0lCz9ikRd1UPbTSlhv0OlymmjsClVzj0nTnqnXEZMopV1TmelZYRPK9Uq\n4z7vCCckUdi4qiSrF0YHL6peGJtsFSDjUZBqr1ZteNH+MWnmO29uqaT/QglroTruTJrdms9YVoeF\n9aWnwNydeDQ3uIiSwZpeGFmYIIs7SqX48vPRGqPONktQ2Fjkh9v2AI4OoXa/Yos7rNAwwH2HjO2N\nyDXGl2oUNsaPAuIZa9Jk5FFcil6Y8t447wIkhY22FY2BwWqkuShsEmr05Kc8yRQFNQr49nkravVF\nOroAaKYNXUBfm5sIiR1SmTP22sZrAukJDn+HIAW2ylWreEfpFVy8W+AAVTROrIKLZlTFVqqMKNBf\nMmBjgw07koQTlN6Tui4An2yNtSoUahNf5bRessXkB3sppmyFsL06p0aJIxKPQpDK0mhcVc71qqCo\nemEUFcloexshkGaSZjVgI20v0nUB+DOWRsqKemFeCltse3VONyLAjTa+/P39NQ5WCpviS7VnZuIb\nRS/MWyCjzliSRnMrXIqVGNGt+8QWOjQ6lyeOrd8LK15se2bV9qjEI0thq/o9Ps016ZlNBXSJElf3\nmfB9GlKWPBMpVKa5sHr8DpmioFtT1tstVLhDviKQXsdTGbWtNs9f9cIPGaehB6kxjYaFY9ff353P\nrfukzMe2mF5zuggsF13VRWAc+Tgfty9sIkDTOIn32ZuQMqNxJNtjqUPeC92ZimTWC2PFTon3ZkPC\nk9kK3WbFBb11jfSFju3YEdheKQU3ZFvaW/a9WRdv0Ct24IltxZuEA3hbuaOfWe1I5qOwsZ1eRi08\nXi/MRWFjEZTuZGv1e5QvpW1FQzyyGmmPoRfGXm5YDUXaVsy+ucaxvM5c7EfH3+9EgbM0YS0eYS/a\nzrh4jBEZ3+xHtjK2vD8MOAz8XYCLH+IzdolmMDSFjY6LvYlHaT4WhED7Zv7OJ+mFETFi/Ww4H1Es\nusbx8p74GYyTOCkBudxJtBxG5f8QQonHObnLCF3xY1EVJ4FXlrZHfIcHngZIwZ0PnK4LW9nVaXvE\nPu8VPQ1uXxgI56gF4UumsFWM83dI7jNpe069MFa8mK207Nj5BE2SHaGBJFHiDmyVk4MnVxh4SHvZ\ncN/hSdfFTKOJ5hvnJGm99YxlaDTkew1wkHGA22fGVqQzlkymjJVd5jzkKWysXhjjS1WUZ2R7wBg/\npVX2eQAAIABJREFUKH6ForAxfpTWdan7zGqu+SrFuz2n68ImAuq7yp8PHltRaDlMjFjXqPgViu5p\ntD2lE9TdgdeyoWLEus8mGs3ZVsgzljkfCF+q2wrrp4T3hog7NX0hH91zpxTQzb5USaaEtlKfmfke\nFVuh75DEd8jqhR34fWa+w0qZjXR0FQobc4e8xvGaQHqC46x/QSICIurCQhQ2TqCtVug80F+2+nUO\nsLhKbFiFXfEHaEUsMEGq4ihjTr+CCBjM8Ol4vjqns5rNa5zwMFiAr+xuN+vwc7xeGKvrolHY3DQa\n+r1h3m2ycqNS2Kjz5hDrhS2FqqBsZcNdRvjzwWt7Sjt2ppottXcnv8Ptuli/QzY5WoNUd4OLN0uc\nsUwiwOinVFpO9MyqXpjTT8lUJNqveL9DylbYTlA7b0y3FJ0req+rXhhre6VwCR8ONao9MxsneqmP\n3pjudqG7QHg+iDqUnF/hpBPofWbjWPbMVvTCiDufqhdmlUERYndGL6zaEquR5kLmX+N4eU/8DAZ7\nyB8OA3aCrouTwsa29uWhul4Km/zMwWFSSuEvYIQIZl2j0jmGrlaRQVucqBAO0F0sdlrX6KSw6YKx\nvmTr6DTIfSbn43RdRArbI+mF0RQ2Kkj1CgNv14UKNmRdF5bCRr83XvFiJeiNkR/L0IR5apOPas3u\nM1vNVgon4+8n0TjGZOuJGk0UJpzfoXufNb2wGEEJKMlW0vZEvTAGQQlwCSmW7inrhZlEcs+XYq94\nMYeIJm3liHiMioJKjFh/f399PEqKobApqHKm8QGgJB6FhDWhF6aL9nNJLgp1xT4zjQ7m6Fd3ajxC\n33+MdwtBzkKjCXv2mb0LaM/8qoH0Op7I2JCOkqW8uPmw45yscB53oVM1TtjWnfSFjggqlWfmA31f\nIuC0z3RQyWrjeJwQoASpHPSX1QtjdV1UvTAO4s0HG7yui5CEC95tTS9M0XXxXehUvbBY14VLmqkd\nyazdbUjqI6+Rxl7oOF0XzVYICpswH9PSHji+N8ZCB6sXdvJ7hMg3wPmpWzJpxvrS0wUs+B43a63r\nozXBvIt9qaYXxuq6kBR90vZYvTBW10XRC2NaVgO6Xhij/6f4KZ5S7ztjN6vCJWf2Bz5GdCYeRcRQ\ntM+KXhiLDmY119i7ALvP9N1C0dYjEwGKXth6VbCK/IDZ9hS9MMaXbmTbY+8CyvngKUwspaP7mkB6\nHU9ibEljPel9GIU6d6y2hJo9JiCSFHf8wFcxAO4A3dEVuhUHJSZQYeN8rM4CL+A4/v44SB2YIFVp\nY0k/84rUbeD2mdULO9mKUTh8tx949AyV1OPooyyF7fzMviB1R/Hl/QnrLWl7ql5YdD7w8x1pNCSt\nl9XvYfeE03Uh52MviFKb23ifq14YA+XfkfQKtkPQjr3QkXph9HyK7Sm2Iu0z897wfop9Zr8v9fp6\nSuNE0K0bfz93KWb9FLvPFFKW1IVT/MqeKO7ciX6K06Hkkh+0XhiNlCWpSKKWZ7QvwzAcn5nVzuJ8\nKWsrVHJG9M2RL2WTKVvBT+0OcXOZ+jv5+xSnB6SIuTMyB/X3M2sM0T01IUVpygr3KWfCmixM3J3u\nkEa9MFLL89qGJYFUSvn+UsovlVK+XEr5kYl/f1NK+cvHf//bpZR/4+Lf/tTx73+plPLvOdbz3Afr\nKOkDVIJwCq2CBV0ExvgZcVK1NTlzgN7SiQCes8tSm5wHKE+vUOcjKWxsYG6msAFCYE46Xp4G6BNc\n5Olc/jbd4+eJIJVIfshd3RagsMVUJE43hUX3nBJSZDWb1QhghYapxAJ5QeRbk4v7bEwEKBQ2Jqmn\nUhUjP8CiRk9BL+VLBX1Cck8KQZndrgulF7YUhY1NfjjF11kq0q2YeAyr42ThRNUL457ZTFXccAUt\nlcLG0aWEgpZAYWPmU3RdXJQzuigoIfNJOQvW9kQdyihO5Dvj8XcBzZeyMaI/puM7KHv2ebUq2Kw4\nvTCFwqZQ9F16YXJCikSBvyKQEqOUsgbw5wD8IQCfAPBHSimfuPexHwbwz4dh+N0AfgLAnz3+7CcA\n/BCA3wvg+wH8N8f5XvRgX1w+0BcPE1YjgHRCrK4Ls0Y6MJe1bHzVbDoRQHYIYml7rOOVdV2cF7oN\nqwfkreCzui7qM3PvDX+hY+hc7AXRvc9sZzxJL0wJzAUKG89v94hoS3phe7ZjIWcrt7Tmmkqv8OgB\n1TmtFzApMPeJk7LJVvZSfJqPpkZ7acI3a6bZAydOKtM9TboudU7Wl9LaOAKFLX5vuHjk1GXJRNcY\nP+NGlR/32dQdT73QsZRUXgPJHCM64xsSPcOiuBS9sFuCPgroZ2yIrhOTrWzSjPWlzg6pWozI2x4b\nx0bPzOro1jmdFDb1mel9jhBIuyqP4Une1s8wd75rG44n/l4AXx6G4SvDMNwC+EkAn773mU8D+Pzx\nzz8F4PvKGLV8GsBPDsPwdhiGfwzgy8f5XvQ4GwIrIsdqnBi540K1inNCJH/1FLxw+hzsZYTVYaG5\n6EwigOZRc5WWDfsdkoG+Jk76WLoubLBB6rqIl2ImSGW1s9hqFa9xwu0zqxfGzlfnpM8HOmnmtBUt\nMA81AhS9MPpSrKBxiD056oWF4qTk+cB+h4fDgD3Z5pbdZ1rXZU1qnJCJR1YXgQ30FZ0F/nwgmzOQ\nxSJaL0xO+huFw1lfyuq6iIhHRqvv8vPRfGGBTNEfIxPMil4Yo+tyOhNJv0Jf6B7Jl3K+XtULY3Xh\nuKJg5EsVvTA2+aHoAY2fj95t7kw8F4u8hVCGwkbrhZFi7htSzuKWbiJE6kiRFNy6RtqvPMIZqwMv\nyLuFUUf32oYjgfQdAP7JxX//yvHvJj8zDMMOwL8A8DvIn31xgzUEWqtCCDZ4XReWO87D7uvno/mA\nOMCS+KuKlg1J23NW/Nh9Zlttsrou9TMsYsgJGWd1XXi9MK2KYRVDVmyFbCHLaV+IF7qo/Sk5X/0M\nZ3s8hY3SbaBRV2LCOqRrCEGqVPEjz2ymykmipHhdF7LQIQSpN2tWA4mjNrFVzvOZaEbrOfXHWI20\nNakXRlZNaVuhdV04vTBW1wWAoBdmRuOwui6k7bFJekkvjNQk2a4L7VdYtDHgQ7aeKSpeX+o8Y0e6\nup+iz94FaF9K28oSqHJW3yvyUxqFjdLCY88Hkg5+J+gBMXphJw1Yk0Yaq6MLLGMrLK2w/v5ovsvP\nN9dH6uhKemHkXeDaxrN54lLKZ0sp75dS3v/a17722MtZdLCOUgk22CqBpOtihDPygTnpeE8icnGQ\nKvGUWY0TksLGHaDqJTaoVpHoHqAmK4waJ3T7U94JAYSWjZiEc8KTlaCStRVGL0xuTR682yx9tK7R\nvc+s7XG6LlyQyiICJL0wMvmhdClhOyDWz/cGq+uypf0UFwDW3+mmsDEBoH4+eChsyySsNQpbNKov\nCzUZRQpbVNlldV3qnKyfYnVdWO0/QGhwwSJlrQhFb9JMiZeAuDDB0kdVChutkUZSH9kk3N0+1guj\ndV3kJJwvBmN1XZQY8WZNNHtYiMJGifbTz8zbnlJAZ/XCeJSnD1Uu2YpTU9acbGV1dDXEIwdCuLbh\neOKvAvidF//9nce/m/xMKWUD4LcB+HXyZwEAwzB8bhiG94ZheO8jH/mIYdlPd5wDrCgR4EcEjLB2\nJqj00itY/qpb1+VcxWCDSm/AdksEG+eW9l6n4RYvZvU5vLouy1Q5nfus6IVx/Htxn8lkKx1sUBpp\nZjFkWi9swM2K03Wpv7+/PrX6xVFKeG2JBS50dCLA9B2KCWtO38uceKSDSm9gXkpZ4IzlKWzS5cZE\nYWORcCyKq/5OWuPEmpypCWaPXhhL1xjXqGikmWNE0u8B8QWMtRV2vqrrwmrZWMXc16ReWN1nstDh\nQq7Vz/BJOK+tsMmZ+vnufGqDC9KXWrWzhCQcQOwzrd/DJVvZwgmAY6c4cp/NMSKlo0smW1lNWVXC\ng/Gl1zYcT/x3AXy8lPLdpZQtRlHsL9z7zBcAfOb45z8M4GeH8db8BQA/dOzS9t0APg7g7xjW9KwH\nX63iA3OevzpQ1WyWA6wo8o+/35SBX3EHKMu/ByrXmxTqJDWQAL5rQKjrsmJpOZyuC6DpPlH7zHLH\n5cSjS9eFQwQoui68HhBPpRw/zwVYoa4LqRemBKkbOhFAXugEnQX2PKy/P5oPiAN9Ldjwi9oySX9e\nL0y0lVCrTzhjSb0wVtdFuSAyQaqqF8b5FS8iQHlvqPOL1CRh4xF2PlbXpf5OZ6BP64Wd1hjZCnm5\nIf1U/Yz7TOT0wjhqE6vvdXu6FLNansF7eKL5OOMbzk/xemEcspX+DoW7gKK9yWpnsfNRWqOk5MYt\necaeKWxG3SchCcfGiOMaXQV01k/xFLZRO8sXu7N6YXyMqKKDieKOoBfGvNvXNjZzJxiGYVdK+eMA\n/hqANYD/bhiGXyyl/BkA7w/D8AUAfwHAXyylfBnA/4MxyYTj5/4KgC8B2AH4T4dh2M9d03MfPHqG\nDzZGXQTnxZ3UODloSJIomULrutCoChXFFX+Hu/0B/8qb2LQuM+a938/qupypBmy1ikMERMmZOidb\nxeA0TryJR1rXhRU7lXRdVvjNt7vwc6yui6oRwFLY4vdGQQRwF3dFI43T5+Dg03J1nKbt9b9DSddl\nXaigd0cm/evvDPeZ1HWp32GsjaNR2CiNE0FbgrrcCPONn2fROD6q4u7A6brwemEahc1GKSErxZqu\ni6KBxKGNxzUOXb0RHtlKavVJtsJqIPFIWSpGFG0lem9YTRI9RuTim6/fxVcONply2ufDAd+EdkNp\nN7JV0ifcCL6UfG9oxCMp7TB+ntvn6DtcrwpWRHFnfxhwICmzrF6YoncICGcibSvcfN77j6CjS/op\n9jwEBFtx6oW9UArb7AQSAAzD8NMAfvre3/3piz9/A8C/3/jZHwPwY451XMtgIeMyhc2YgX8uFLZQ\nt+F0IHNBJZMIUPV74g4bA6XrIqO46MuIF+JNVzEoXRfOUbK6LqxemKrr4hblrp/vDRaerFLY2H2m\nAiyyOs6KVtLUpppsJRMBrla8qq5LpIkALEdho4NUcj7WnrmKH3/GVr2w3vl5t+MFqsfPs7bCJWis\ndE+hsqu8N2yCObok8rQcJb5huxhp8Uh06a17Ejd7EH0zuS+UL7VT2DRb4Qta0XnDxYjad1jwL7/B\n2d6HiaLgKfHopu2ZKWy0LzUmrNW7AB/HkmeiSV/oNB9pe5JfoQsTnG9mGRjO4i+ro3upF9Y7P5UY\nsX6+N7RnjvdZ0dG9tvHynvgZjK16oWMoZ4Lom7Xd8kIUNp7O5TtAWQobe+DRXO/9gdJ1USlsfPKD\nfW9886mXG57CxtqKbz422aq0JgeIQHohXRdWp8mdNGP0wkZb4Wk5Xtoen3hk9pnteqJAxuvno/nG\nz7OaA87LDUdhoxMBgi9lLks0jcZ8GRmGYTxjSaoi36bbqTNXzwcPhW2RCx1rKywSdaHkLR+DsaK2\nvsTjqOHmo7Cd/FRgf6VwxR2W2lQ/421pz5+JzqKgQmHbrHhaL+NLlRjxMQpkQNWh9CHzWYo+XQit\nfoosoMct7cn5RBAC/95wtsfqhbF7PH6e031i6ZTR+aDo6F7beE0gPcGhBC+Xn++NLWH8VdeFDbCW\n6cLGwVaj4IAVJ2VF6eoal+HLx0GlBrv3BPp1jQyFjU/4CEk4UgSzfr43FLrnzTqGZN8pQSqpcaK0\nJr9cQ2+NlPggrRemQX+5BDP53pB6YXxrcq1S7Eqqs63O6+/kUKMkLYfUC9uRFDY2CXcr+CmeikT6\nFcGXclo71VbYRKEpYU2iewBNnFRKWNOIgP6crF6YpOtCvzdaC3pmnzk/SuqFkbY3fobUCxN8KUU7\nNlPYTvtsSlacqU2cL+WQJGxhlZdOYIsS1HyCdhaLeGQpbDWZEjeD4QWqASKmq/EIlazgizus7VG0\nPVLMXb3zRc8ssxFIX8q9NxylXmHaOG1lZ/alyl3g2sbLe+JnME70CivEO27BKOm6kKgKPtio+hzG\nA28d02iUC53Sgl7pbsNo2SiXYldrcuDI9WY0SYRq9v4w4EDoGDwGhQ3gtCBUXZclKGwh/Yq1vc0y\ntrfEhY45E9kguv7+aD5AobBxyDVW14W1Pe29iaqS3D7T59cpYGOpBk4KG6n7xL434hnrQnnuFD9F\n+L1xjWziUYtHKHFSwlYkXRdSD0hp0325huZ8e07X5UxR4RIBLr0wRddlnM9IE6bPBxGhaEY8clo2\n5HsjFPGcHRDZpH/9DGUrJIXtZCtEcUfTsmHjWPJMNOoB0XphAn0U4Arom1XBikSuhXsi3vl4Cpuw\nz9G+iDq6bgpbHBfztndt4+U98TMY1VjekoZABeabuLqkXpY4/r2mSRI9s9R2UkCSWJ+ZRl0d95mA\nrSoHcvzMWvIjmk8NUgGuKqlR2LyBOTsfJV5M2B6gX4oZCpsSpFptZcMLazr32f7eiJcR1vbYNUZn\nA6Bf6JhnZnRd1quC9argdt8XoVWpSFwnKDY5yiJJWIFXP/WREaFl6ajjGr3dcpawFeqZF3hveF0X\nxVYW+A7JS3F0Pqjz7Y8o9O6coq4LfSYyyQri3V7kvBETPswaleStSxu0fiaaT9F14d9tLUnvPh+Y\nOHv8LKnVRwuHczHi5Rp68ykxYvjMC1HY2BhxXIPnmZexFf68eaWwvY4nMc4VPxI6Z6IGqPx7hr/K\nwlbpKqcqHB4GQwqFjaxyktSA0zMTVUkW3g0QVU6RlsNW27VnZrRsGCekVauoZyYqLartMVXOW1Jb\nQhELlNp0E3sC8HTPaI9Pui4CIoB5t6VuOTQ82VPlVOij2w1bKfbShEc6FxcaMGeiQmHbbjhEACvU\nqVQlFQpbLBzOU9gYW1F0XSq6h9ILM1aKWV2XOmeMGuVtb7vmBOcVXReA8M2sZiSJoJRsRfDNFIVN\n0Atj/SjAoIP5M5HxpedLMYkqZxCPNIWNRyiyMec4H1nQouPiCPVR6WHuZ14AVU75FcZWlkBxcb6U\n9SvsfapKePhR5RxyzWkrtzteXxXg7haXn4/m5O8WLy+d8vKe+BkMGYpnanOrtiZn16hBdWM4I6Pr\nAhz5q+Zs9BIUNkbcT9N1MR6gRMVPrX6NaySqVRKPmgyITJUWFQnHtekWO3YQjo1ZXymF0oJQW5Pz\nSDivLoJSrYr592o1m6Q2GXWk6AodfUHkqE3jGpkKnUhhI55ZaU1+uYb2GlVdF44OzlI2WF/P6bpw\nlJIRHSzYnglhUOdkC1o0DZDQC1PpnpE9y+hg4vwCyGcmZASUM5vW1hNbk1s7QRG+VKHluBsVsPvM\nxoisXpiS/GCeWWp1TqI8+cYHXBx7opxRfoU/Y1k6lxdVLiBlnWesiBqN5lN0dLU7pK8oqFCjKQ0k\nAdRwbePlPfEzGHylWAsOaKFOc0AkVTmJizsdpBKaRbu9FqTytByjUCd5IFdKCRtscMmKeD5FlI5F\nz6iBfvjeKF3ThACLvRQvEWxwiQAeSeJsTc4EL6qwORAHlW5kyk7RuiLOG7nKyeqFuQM2wpYB1la8\nF8T9IkEqq/dBzicnHn1BL/3Msq4LoQckXG5cYu4Al0wZ10juM5lspXVdVJFcU7L1fMlW3puYwiYl\nb4l3u8Yu8RoZKQbF9gQhcuMzszFi1QuLO0GJhVDCj9bPRoPtJrw7sIVVP01YS6YYxdf3HIWNtT3W\n19c5+TukqdAhxXTkHZLW8qxJfzfqin3mVwrb63gCg9eyMVc5BQrbKahkHKVTc4DUdalz0gcojQhw\niiHzHTYYeDcAqs3tUo6XguqeOn7F8GR3lXOzinVdALYrkhZg3e2JFvRie2SXrgvAJj94qC5le6Ku\nC+DTCNC725CIR9pWfJRUVauCo3MJZ6wT8ahcEMnKM8D5UqbKee6mRSYKSV0XpnAy/n7m3eaSrW50\nMEtRGefk27G7tCpOui5k+2bAl6RfrbgW9IovZVpMq0jZyzX01sh2kXTOV9fItibnfClPy9EKZB4K\nG8AlKySkrODr3TRhZ+fM+u6zvpTuMsucsasVDoSEhzvxyFLYAM2X0uhgWuPR+N6QOro0qlw4E5lO\nuMo9/NrGy3viZzBKEYMNMqh0itKdAywCEaBclkw86nGN/AHKOqFoTxRdl6UQAfQzmwIsBd1zI2gW\neSlxAnKNCdiUQH/N64VJFDbimfnEIxFgKdoS7gviSrmMeCHjAEeZ3RBtbk/PTCasxzV4bYUJpJnk\nDMC1R74Vg0r6ciNoS7iSZhvyPVR0XUZfzwapPIUtfLdJXZf6DK7kLUDus6rrQiAglPkA7pnpeIQo\nTFRb4URovRS2U2HCLBxOocqVhDV7KWbRf0xxh/Sl9PlAXoqBBVrQC3Exd8nmUeBUXCxoPDLNHgAN\nYc0iHrk1DmSMyM8nFdDJZ2bj2DhGTCSsCTScpOVJ+D1ljYwfBV41kF7HExqco1QpbEYnJFWXfPOx\ncEZAq+y6KCVnyouzciPAVplnlg55RsumVjHcVU7nfKKuC6vf44Qny7QcT/ULINEzkq6Ll39PU87I\nfeaDlzFgo4LUDUE1EDsgjmuML9pawOZLBFAJazURQNO5fLbHdueqemHsPvvpFQ9/xvKtyVV0sDce\nYegfAIsK45K3rK7LuEaGqqig6/waJ5c/057Tjw5m9mSck9hnSQMp1gsbhkH2zQwth+3a5KZfcdRH\nTScGiPXCeMQjZ3uszME4p5Kw5vXCqKS6ECMyvtT6zEJBS4kRnRQ29r1h9cI0HV3lDvlKYXsdT2Qw\njlKrxApVTqrtJOkoWTFkMkhVKjeKo2SSH6dAukMpUekaAOd4+eRHDLlUdF0ojZNMZZe4JLovS4+n\n6xI7Sk3XRRBDdlLYHlPXhT0fyKCS1QvbCbbHiPYvkghYRLTSeMaKWle035NQXPE+8xc6Dh1M67pQ\nrckX0EBSk/QmXReATLaKui5RC/oUnSs6w0hdF4At4nmTrbnzhkgEMDGiOelf1xiLNXtt5S4TIxrR\nets135yBE71mkHCa7V2uoTXGJBxPYWOKeNZkyoFPMCtnorMxyu7AJaTGNTJ3Pjc6WLtbjD/juQuc\n9cI8MSLA3iH5wuq1jZf3xM9kbKmgcoTVrkztTxU4owRPNh6gSiKASaZkEj69tpMKPJKv+IkHXtR6\nWKGcMRn4qp1FZvSBuEOQ3t2GgaDz742zFSgTVJ5sz6w54K1y8toSI/qPDFKFRACXbFWq2UwFkbc9\nd2vyy5+ZGiNllu1SUhPgcVBJ6yxQlxGx68mhTymREgFm7aw6J+NL+cSjoj+mFGPifWa7AXLzKbou\ncYtptQNiXUNvfeNneVuJ7Fkr7jDVbE3XhbFlQGuMwryLXIzI0cuVC92WtL3L399fY4xETb03BB3c\nqRem7fMjIR5J9EzVC2N8qYT+Y+lXwv2nFyfuDwMOA+unWGo0R4mra2T32dYkRNDRrX4qtBUhBtuu\nCU1GBYSwWRHr4xOP1zZe3hM/k8ElfJRAfxnBRcr4zVVTVteFSaYoWhXMGjOVmzhI5Q88rtve+Pu4\nrifMAaokApSkWby+seoQX0YUXRcGPaPqugBB4lFw5EpXJC3YcJ4PcQB4RlAqiccogNGq2Uzyw/kd\nSq3JN3HCen8YMAwc/17rEGQMUsWEdaQXlmlNzgSBbPKDuYxoui6FPmPZzpmXP9Ob0yrmbkYHq63J\ngf6FLnMpDveZ1HUBRnvm4iVO10US7ZcSj5545EQpIZJc2ntjLAoKqHIlaeZGQbC+lC3ixWd2BsVF\n+FLnPu+EJP2GiRFrHCvoCXZ8aaYoGK5xx6ODGQmP8xp5dHCvuLNE4pHV0R3n5OJOhdHh1NG9tvGa\nQHqig7rcCHxYf2tylr/KXejWq4IVwV/VkylkBl7pFNeZU6JrnDrZOSlsRJcSSdeFOUATVU6Trss4\nJ7fPStXBTeeqa2iNnK5LvM985Yahc6m6Lg8fbEgaaeR7o6C4Qtpegu7J0CucVU57Ailzoev4Po0+\nStqKchkhfamCXHtoREBG1yWmRou2QicC+Hik51c0XRc+2arQPWPKv2h7hB+tn42GggJnLtmVUsIh\nU3xoHEnriki2KkhZmpZzEN8bIkYspK4LlZwR6KNbwa8oNGHGlzr9lFIgU1Dlit4hpxfmK5BpxZhY\nLyyjo/vQzRncMaLy3lzbeHlP/EwGk/nUnJC3vTsTYB0EXZf6ezlROiXA8omdMgGWVrlZ4AAlOrNo\nui5mPSCWUiIHBx4BR4BDcWm6LvE+ayiuJXRduEssrevC2IoQ6J8CrEj02px43Cnwaaorko5Q7L43\nok7M5Rraa1R1XTjNNQ35waA8jbaygC9dQrTfnXhkCiesXpgc6LP6PVL7dBM6mLwUs7ouAN/IRCt0\nRImAZbSzpPMhTASoVCT2QuehsJ0bHyjzMUhZY0Hr+N5wHclivTAJKcsmzYTzgW2A40QbpxpcMPGN\ndLdwxsVCtz2TbMm5IG/WSJPYA1GM6L1bKDq61zZe3hM/k8FVl9yQcQGqS1SKFUV+gOSvKpdiAcIp\nBZWdfVmiWqVCvN1JuENAKcnouvTeG0XXpa6RQUk5NQc0XRfhciM4cnvlhrA9FqbLPLNie3yVU6Cw\nkeg6a5XTjAg4BfqEPZ/bsTNaNkZbkbrbxPus6n1crqE3p7uarV2WnGicONmqzFc/x9Be2PdGaUGv\nVfCZ88aYTJET1oz2n+b3uihPKREQFzqqrouiJ0ghZY2IgMyl2PbeKPRypTmDManHJEfPeofOxKMZ\nlal2QGTvP6ZijHbe8Mh8twaSoqM7riH2K4+howvwtD0no0PZ52sbL++Jn8lgDYEP9GNKye0pk+o6\nQHl4JMAnfJTgha9WeTPwDGdXqfi5hToVJxStMRPo9+ZTdF3G38t1v7ImW0Vdl/FnGDSOZ746J6/P\nwVV2FZTGuIYHvtDJ+xxf6CTNNRoy7kWSMPZcW9BzdC4hMCdsb7PidF2UM5YVcx9/JtbTUHzYoIQ5\nAAAgAElEQVRpTCnRKsVWdDB1QeQD/fp7H7q7zXmNnsJERtcl1rpSdF2YJJxCr2AoJYlLMaHrIiEC\njFo2bKFjXKM3puOSKbwGkrPTpZaQ8hZ3GFsZhkHUsuGS6oqtOM9YZp9PzWVICQ+mBf3dnuuACPCI\nR0VHFzCigyUJDzc6mE+2snfIVw2k1/FkBnu5UQTVgH6A5W5NrvDv65zOKsZSYoFdDaTUBdHpKFk6\nF195BpyVXb765YV4+y/F7BoZx6vYHksp0XRdOG0JuoJIPLM7gaTougAcvWIM2HyXbK01eVyVTCFJ\njK3Jl7A9gKtyOhOP0oWOeGaFJnyzXmF3GHDoUkp0uieFeDT6UlXXhfFTiq5L/ZnmfIqui5Cwdmuk\nybZiutAxMeKJomJOfigXOha55kK21mQxRWFbce8N25ocIJNwmTOW2GcJKdvTHxOolOPnGAkPsWEG\n3RjF+8yMrZxb0Hv3OUrOaDq6sa08po7uOKeXJuzW0b228fKe+JkM6gAVq5yADxFAVTFECttmxcFM\nNV2XWMtmRQep9TuM6Vxaa3LfgbehEgGKrotQlZToFZ5Av85JtayWEplOXRdBqJO2FWKf5QudEQm3\nYoJUXTi8F2Apui4AaytKsDHO10V57oTzQUjC0UkuM8SbS1jzAeCGCCozQp2xrQi+dAE9IAD9TlDC\nmbghLrGKrktdo7U9MqXPoei6xL5UoXPV9zBMtirFHbOuS0XEcPo9XsSjtSi4gE4mAIrCRtmKEMfW\nFvTU+aCIIRMxouLr6xp68wFkcxkiCacIm9c1hgkfBSlL0IRrEY87H7xJuPp7nckPKr4RY8RxDaai\nIGF7OR1dRoicv5NGEh4KaODaxst74mcy6Aud4ITqz7RGNTwfB5gPXgBeC0LLwDMB2xIHaPwd1hb0\nbgqbW++j/kxvvsvPzp5P0HWpczJBpUb3jIOX8bOearZK99xSz+wVCxzpXHylGOASj1xrcj6RqVDO\nOFoOPx/dgp74Hilajnyh4yglVlqOgOKibCUh8BrpuqhBapwI0KqcAHcmUu/NJta6Ui83HPLDe8be\niaiw+jPt+Xhdl3qGOBHRVKEjgQjgmjN4EAEKJW6c06tlwyIC1qKuC6cX9ji+lG3OoKBQAbIFvQn9\npxbI6PNBjBGZrrDc/YdBriXQwebiThgjijq6dQ299QGqjq4PhMCiymkqpUAHZ/flmsZrAumJDrZ9\noKplw9BoqMCc4K9WQ5YEWcOKn6LrQlQ5MweoqVVw/Zyzms2/N77E41IUNjrAYqmPwp6ElJK9V9dF\nobABJJJE1nWJbU8J2AAy2KDOG6HK+Ui2p7WgN+sBCeLmbkQAlVgwaq5VRICm6+K5OAA46oXF1Wz1\nQtdPjiYobMwF0Yn8UBGPjHaWrOvCNPXgijvbINmq67owiIDHu9Bp2jjCe0NpnBhR5QLiUaGwKXGs\n25dyyVu+UFt/pjdf/d3M+gA2Ya3YSowC5/1erBd2KqAriceun3rkeIR+b7S7QD/ZqlPYXCh1gCt0\naKjyeJ/V5Og1jZf3xM9kbNaF7EjGV4rHnzFpS5w6+niDDSfE+2blV+Sva+jNd/nZaIxw536Qqguy\nEu8NHbwo+0w4DSl44aH8zmr2KcDqUUoER36i5XTsWa/QxeeDKnr94Lay44MDhmqQ03XxIR435JnI\n6rqcaDTGZ2Zg7aOtmBGPLFKWasfOB+brVcEqECdVkHDj57yi/Wc6BBOkmhABgq4LQPpSc3vkjO0x\nyDUX/Uq9IG5IpKx6xjK2ItFyiETmY2mkbdZl7AQXdIVdIgn3WHTwzYpANAm2txHo5RSFzSyPUT8X\nooMF5BpzxtYYkSkKupNw9XPW5gyb+C7wqI1RVjUh5TuzN4Sfut1rXagv1zG9xlcK2+t4YoNxQjsl\nMD++3Luu8S9DYZOCyk5gUH+f4shD/upBQ/cAZ6TD1FACfWB0RFwVQxBkNWbglX1mKmAcLecYsAlJ\nLqoFvYgI6O2zouuyBIVttBVvNduJJFEQAVqLaSIB7kQESNXs+Iy93Q+4WZFB6joOsBSNE6CeD24K\nm5N2TBQ6BArbOGf/3d4lbG8X2F6GwkY1Z1DaIwd+7/Kz4ZzEZURDDBXcHSJKiSbmDvi0s+qcPVtW\ndV3YDohK50yARDzaEAEaWm8JdDDgK+4oiEcJwdz5Dg+H4YieYQtajKC03hjFhWxdApm/DWyvrlFP\nBPRtRUXjdGPEhShsTvqo/y7AI1urXljPl6oUtpsANVrXqHQDBPrINUVH99rGawLpiQ5O48TrKG8F\nnQUFSSK1nXTq95DwZOd8qWoVJVDNB5Vx6+FElZOggDyeUCfD9fZXJa10LrHSEqFncrou0Xuj6X0A\nvmdm9MJyFT8G8ag+c99WdISBV5/Dr51lRP8JQSpbPIl86Xk+I3pGEnjlLu6srouiF+ZHB/PzxXph\nmt4HwD6zopH2sKhRzVbMiAAikXkro7hYLRsjVXGndW0C+oWJWxFhENFoToUTM4VNiRHrz/Tmu/xs\nbzDoP7WwyiEU+QQzpxemNyWiqE3mfVZ8KfXeiCCE/jMnCqEMEo5OPLobXMR6YYrfu7bxMp/6GQwu\nYOOrGAx/dXcYdV048UGhWiUI7zoz8Exl103LqRUJRVuif8kWKzfuYINxvEKlRaGUPKauCxC/N/x7\nvUyVk9M44bnjcdfHRJWToENIFTWTTkz9nNdWiH2WqqZ8YO5CBAzDcNSyUdA4Q4gkUSu7/QuYppEW\n7fMp0Bd8adyO3UtFkvzUIrouZo00smW84keZ+S4/G43Ir2QSUiEiQGxNfrmOqZHRdWFoe0qBjEEE\n8HEs4VcSqHJ/4tGLeAwLqzv+jFWaM7At6CO9MD1hzXUTdmp5KokAptChoEbrGnvz7Q8DDoOavPWK\nuQMc4lGhxTEUNjvqSu1CHdzRXhNIr+NJjZtNiakGSuWGQs8orYy5AxlYoFolBpV9KlLGafgobDfr\n0qUa6FUMbwb+TNvrB1isrsu4xn6AlancULZiDrD4yk3seBVdF4CocoqOfLsmIeNqlZN5twV77kGJ\nz8/M73NvPkCtZjNVSQXxyKO4XHSu/WHAIASpWzIRICdvieYMSpAa0VEBAdFEFTq81IDH1nWhbMVc\njJHQwSeR3DjBLFGjTeie+jmGwube55s11+xhvSpYryJbSZzZTlshu+3x1EfmvVkq8Si8N4HtLddN\nWDljfYVQBimbQURHlDOlcFJ/pjnfKY7l9f/caOOKRO/Nqd+njLYSFHdy580CDAwTqvzaxmsC6YkO\n5gDdmZ3GrdC1qfJXmQw8m+TiqlV+/Z7HDMyj6tIJmSIkZ6gMvNBxCAgO0AOv6wJUqmKvirGAUKek\nLcGgZ7zVKp0v769yRnpht3u+AyJT2c10TaNaTAvV7LDrY6aaHV3oBGTd+DM91GhCZ47ooqJoII0/\nZ7IVpW2u5Ff6qLD6OXY+ZyKA2mfBT53F3I0aSEEyRdZ1Id4bTdelJh59yNZonzO6Ll69sDjZqsSI\n45xB0kxEBNCUEiONRu0+Os4XxyMuiusZNcrbyt2+rxe2kzogMnQusSgYJQKEDogAj6R3aqTtDkqH\nZ6WYrO1za8hIOLJzrWp70X0K8CGGFpGz2B8kVFj9mdZQYsRrGy/zqZ/B4A5QPy2HPZDrnA8JGT/I\nui4xf3UMXoyXJVXLJnK8icrN3b5PKbFXq3ZaBj4MNhLVL7euy+U6WmtUKzcPWc0+X7K9+yxX6II1\n1qo3M6LuV5lnDvXCMoiAQC/MuidyUEmi/6z7LCACFrCVSC8sh5T1oYMVJAkzKqWEobBJgXlAGwI0\nXRfAmXjkKsWAeD5QnTOdIrneFvSK3xvXyJ4PZlsxJlszSbieX3Hv85kexp8PoV6Y2AFx/BkP4rHO\n2aWwZZCyhF4Yn5Di6OBOtLHcsdCcTKELWvIdsqcXVgvenvPhVJCnNdLIM1ZkD7iQstc2Zj11KeVb\nSyl/vZTyy8f//5aJz3yylPJ/lFJ+sZTy90sp/+HFv/0PpZR/XEr54vF/n5yznmsajON1Q/GUKuc4\nZ8BfzSACiCoGX/3yIgJOVYwuYiiTgfdR2PyUEq5axa6vzslQHxVOf2++jK7L5TqmRk7XpY/iuvxs\nNOhLsagt0a/Q+d8bFg0AMInHjICj70JHVTmFM5bRC1NFbaMA66RhICYCwn02J6QAEUnCaGeZ0H8A\nJDQOo+uiX+jIZKvwbnNoHJUC0r8UL/HeuJCtKXpF0ILejioXYkQg9qWZro+9eKnquqhanv04Ue+A\nSO2zSSNNRjzSemFqXNy/CyjF5Ji2JyJbCeTaTkBE84VQcj5CwqPus6QzZ6awjT8XoMrVzroBY4LV\n0R3X2C/+6ndIphGTXyPtlcKWGz8C4GeGYfg4gJ85/vf98VsA/uNhGH4vgO8H8F+XUn77xb//58Mw\nfPL4vy/OXM/VjJs1x1+1GwKZmQVi9Ew9DNns8YYM9PVqtimZsmIOZC965kyJI79DoiOGouvCBFiK\nrsu4xoCKlNJ1ae+JquvCCsRbkXDiM2/IKiev68KcDxkUV99WlkA8KlB+il4hvzfuyq4X4r1MlbNf\nlaTpV6fEY6w5wFJmWb0wlc7VFQ6XurBxCWvFVjYsQtGUsFbn2xAJZg0pK1AfaX8f0bl02wPQbUGv\n6LpsiEus8h7WNTIdV5UKvpeOyvlSxY9erqO/Rr6I5048ArGtuFGeEqrcTL+KYsRhGBbRfWILJ6sj\nctqOKu8VEUSZA/bdppGyCyEeOfSfkvT3IR43RHyjxIjXNuY+9acBfP74588D+MH7HxiG4f8ahuGX\nj3/+vwH8GoCPzPy9Vz9o/qqQ0Y/mu93z3FAgvoxktCUYGKybXuGlsHkvdKeubo/E2aXmE1BcQKwF\nkdN1MVa/zNBfpQW9qytSxvbGdfQTAU5EgNINo85JVatMtjcMw7ErkreaLSMCgs6ZgIq6ii+INLqH\n0AvbHZR2y2zFz//eKLYSUUpuhWIMp4HE215do1fXhUWN+mxF03XhkHBqswcGEa0XY4LLCEvp3TBa\nV+KFLki2nvbZ1FlKTvqTqHLWVhi9sByytedHtfkovTCpuEOig+XijjFhHSEeT90AVY20vq1IiOh1\nCdE9l787GpFeWCYuBgLGhBvxKMSIdU4K8SjqHfq6wvqTZtc05j71tw/D8KvHP/9TAN/e+3Ap5XsB\nbAH8o4u//rEjte0nSilvZq7nagaH/MjwV72BuVcDia1WeR0l64TWq4ISUEru9qquC9liWmyP3Ofs\nJrppmXRdgKPjtdIr+iiutK5LkJTS0TNuRICxcsNW6MyIAC2ZElxuxMC8nl+tYEPv2uRNPAIIaTSn\nqqSk6xK/N/5nNif9rbaXRJIEvs+PCOCfObSVxJnIfIdWjbSUrotH+w/AUS/MqesSJ1s1xGPsp1QK\nG4t4dKGDl0A8qsUdumuaS4phId+sJ6z7vlSNb7qdM2Vf2k/O6AkpooCeQOsxEh4+DSQ9Rrz8uek5\nMzq6Hr83zmne5+NZ1+oYquvokvdm4f5zTSN86lLK3yil/IOJ/3368nPDGIU334RSykcB/EUAf3QY\nhrobfwrAvwng3wbwrQD+ZOfnP1tKeb+U8v7Xvva1+Mme+YiC1GEYpMD83I490EASKGwR/aoeNApq\nodsismbgjZzdu/2BrqaNSJIgIDqIui4BwkBNppwO0CBoc4sFqo631zZX13XhkHDypTigj+qVFmeV\nswQt7fX5gLhLiUzLCS43rO2Na4yqklrAFmlByGgc6nLD2944J0lVZM/ETenaXqYzHsB0RdLm67/b\nGV0XJ7L1eBlpfI+6rguDDlaRJIEvlS/FT1vXZU1QSnRdl6A1uazr4kVlnm2lb3ta4pFElZs6IGaQ\nt5frmJ5T1/KMYsS1pOvC+Sl3clRFqXfvAodlGqMovnTf0QtbClXOxojAaCt9X6q/2xECfPycaCsR\nUtYoZ6Hr6LLvjWef70Tbq+9D/z6lMTCuaWyiDwzD8KnWv5VS/lkp5aPDMPzqMUH0a43P/WsA/jcA\nPzoMw89fzF3RS29LKf89gP+ss47PAfgcALz33nt9kuMVjMgQdF0XL6Kpztl1vGqFLqA2nQJ9mrNL\nXuhEpxFVTaUMPOl4H6ua7UbC1TVykHH+mWsL+inkV7rKGfDR3TQaSdclsr0lqlXm1uR3+0EK2Phk\nq35xn1rHElXOu/0BH34Tut2LNbL0Ky+FzbnPSvKD7pwpXhD/v28YbSXQCzsjZVV6Rd/2FumQKum6\nLGN7raFrpMXIDxWNwyECxH1uzJnVdYn1whZ4bwREwN1+pJRM+Ta1WESjypVCKNGcwZmwljsgUhpp\nfPKDpQEug3hUi3gHvFmtJ9dXf680nxvZ2k3qVfkJb7JVZmDYEI8kglLU0f3Nt7vmv6s6uh9Itm6n\n1pfV0fXZyjWNuU/9BQCfOf75MwD+6v0PlFK2AP5XAP/jMAw/de/fPnr8/4JRP+kfzFzP1YxNYPwq\nH5YVVLNqICUqLT3+qhq8UILSclBJBKnKpXjVR3HtzJUWVddlQxyguwwiIOjkAPCBefRuy9UvRn9M\n0HUBuCqnhMaJ6BWqrZBVTicVSX1vNpHg/E5HPPbWeBYiZ88bf7ARXeh2e03XZbPiRCtpDRFGn0Po\ngMjphYmi/QF6Ru2AGCHN8kn/PiJA10AikGuCVoyTPrpINTtqzrDP6LoskTSbnvMU0wn0d6B/oRt9\nvXbGRki4cY38u93TCztr2fhsRfalYUFLO7PjBhdi4pGIY3dCkuus+9S3Z8VWNitWy8ZTmMjaXrTP\nWlfYeJ+365VQFCQ1kFjbYxGP5He4Jt7DnI5uP76pn+Pm67/b9axUNZBCBsYrhS01fhzAHyyl/DKA\nTx3/G6WU90opf/74mf8AwL8D4D8ppXzx+L9PHv/tfyql/AKAXwDwbQD+i5nruZoRBVhZXZdIWFOl\nsHn57X3+alarItZA8ibNnFXO9D43EgEZLvrlz7Xm1OgV0WVErI4H+3y60Jlbk3ur2ZkqJ3NB9FV2\nlcQj04Jer3JyorYu4V25yknohSmB/rjGfuKxonuUjmRMwlq+0AXoGTes3Woria6PdR1TQ6cB+ils\ntJ6gkAjotaBP03LCYowXSaLrusRnrEsvTBY2fwRdF5nCRtKEve+NaivxeeOOEevn2PnqOqbG4aAV\nBRlKnJL0B/wFrQjtkqVax+gZNfHoK5CxjA6naL+CeKx6YeEd0owaHT/nsRWZwkb6KcVWrmnwWPqJ\nMQzDrwP4vom/fx/AHzv++S8B+EuNn/8Dc37/NY+wypnQiRnn6xvChz/ko1dkObuti6VaudlSUF1e\nkb+usScWeLsbRCQJJyLH67qY0TirmpAKqg7id/gb32jDVlVdlyg4WESoU9B1GX93vM8anStqMZ0N\nNqbn3Ivig+PvDs6HvVblDBML6TMx0kDyJeFSNOEQNepMPOYCtqhC50Z+6EFqfMa6qpLyfIyfWiAJ\np+i6nHxpQCmR3xuTrss4Z4mbPSiJRwJhAOgaaS1fulTjg2/e8jHddr3Cb932fHP+fPgmvPve3CYR\nAeGFTi6E9pGteozIoPU8SBL1UszohaUobASl3nUmqkhZNvGoaaTFCWun35Mb6gTPrOrojnMyDAwf\nUjajo1vXMb2+BQpkop+6pvEyn/oZjMhRZhEGTDWbHVGrzQoJVXRdxjU+YLVKFZkknlnXdXnADLw4\n32pVQvpChpbDVas84sVLvDeyqG3gKHPUJuN7EwWpYuUZiFvQj7ouKsKAQDyqFfxQy8ZdrTK/N+J5\nU/XCpoaKeIyScFXXRdrn8JmXEep0FWPURECEoKy/S7sUR3phOhKu/tzUUC/FrF6Y7Esf8IzN7nN0\nxrK2wrag1y+IPl2XqAW9WiCLknB1Tj/iUY0RiaIgretS5SwiZL43abaErfjRwWbEo7Exilos2h4p\n+mFXWPHO1/oOVR1dIEZ56s9sRgdHcewT0NG9pvEyn/oZDPpCJ3QpYVrQqwdov5OD3jkGaB94eoWO\noF8dHleoc7teNbv5jPPlMvBtx6tdsuucvU4OKmyV6fSi6LpEyVa9WuXVdQHGoDLqHJPp6tYMNpK6\nLi17VikvwPiOdbuUHDJIkjhIVREBrTUu1Zpc2edtcMZmkim9NaoorhqItd7tvfgeAlw1W+8QFCce\n9aqkK9Dv2x4w+lJdI817yR7X6IlHGFtR6Z6jLw3QwUZEgKrrcj5vAsQjuUa2Bb0/maIXBVvvtqrl\nGXXOrP+md/fsxYgJvUMKuaYlHlt+StWJqZ+NkLJqPBLFiMoaQ3SwmrAO5qv/pqPK+8kPtXACdPTC\nsqgrk45u/WyIDlZtL6BSKmvcBne+JVDlajxyTeM1gfREB0uvYIOX2oK+z181X+h2ajKlf3HPO6GH\nrGZn5vNR2Fg0jubYClHx81W/VF2XiKoo67oQLehzlBIfLSdsQS/ruvQd5dn2/PvMjjjxmNRIsyMe\n+wGRllTnhDrp+aJ9TiYerQlrAuVprXKK6Lqo0KFe6Bi9MFXLhtFAUpFwQO+98aI8VV2XOmfUnMGL\nhEsWtEy6LnVOq65LYHt6jMgmrJ1UJLN2VsLXd/XCVKSsGfEIEL5ULqxGemEHrKSiYPDe1PPGqBeW\nQgdHAvai7Y3riNDBnmSrijYGuBhMs724Yca4Ro+tyPORjVGU9+aaxst86mcwWENQKi0MZ/cxaTlR\nF6N0lbNxyFddF6XSsgmScHd7vdNLXcfkfMk2txEtR3pvzLDVTRDoj1UMvXJjo7Ct+vPVf1OrVU4x\n96jD4Lma7dFAUulc4xr7emF3YoKZScJVfQduPvY7FJNwXf0LPyJAQjSxLehNFDYVFVY/axXtNyMC\noq5IKuJxXGPsSxUKW9QVKYOEG9fhobCdusy2zmwxIQVwydbMexN1hVV1XWKEtRaPhDGdrJHms71N\nkLC+TZ6x1kQAQdFXbRlAE1m+VGMUXbC555tVOYsYHSx1dQt86SkuNiWk6r+5G+BIXd3IYgz7bocx\noni3GD/rPWM3QefMOTq6rfUBPH10RemFab70msZrAumJDlao04sIMHe3UQM2mranIUlciQWAo1do\nui6c1pVPRE5PBLg1AiIknFw1JSt0LgpbVtfF6YTofTZVbk5BrzHxmKJXhGKGvu9QbU3Ot6BXEwtO\nQekoEeB9b1SkbJ3zITsg3ooXsNhPaYF+/WwkAK1S2KxC5KxemJp4DBsfGJNwCTHkXgt6VdfF3Zq8\nftZ7PvhjxLqO6fXl6J49XZfDoBXIomRKRtcFIPaZpbBFMWImEUAkW3VKfd/21POwrmN6fRqKi9ML\nMzdGSaBG6zpa6wMWkLMwFrRuRfroUjq6Eapcbs5g3OdrGi/zqZ/BiNAzTyURELaslkR3+2KBt+KB\nx9I1nC1aM/SK+nOT8yWpAXEVw4f80LUqmEu2dsADnWcW6VyRXlhO12WZZKurWhW1oD8HbL6Eta6R\n1kdx3aqV58BWKi3nsc8HN2q0rqM13+Xn4vm8tGOAQDwm6aMtJElttyw3ezAlbwEQ1CH9TOxSSmRd\nl4dNWGd1XZxI2SjZmm4l3vRTSb2w6HxwxogJ2xt/rq/rQtNHg/nuxPnGz0bt2HUNpHGNntg9tJUM\nWi9ItmZ8aUzdfjxUeaQXNnYk8zdnUAsn9eemhnomRtIOORBCgCrfZzog+nR0o2Rrhu4Z0jNFuuc1\njZf51M9ghBoniSonlwjwVTGyAVskFkhfiiNKnAiPrGvsCXXmL3RescBQDFm9jJgTj31Bae1AjqrZ\nqq5L1QuLbE8OzMMW03q1KtxntdPLQ17o5Aodw7832l76mafnG4aMrgujZaMFbPXnpsb5MiLSckJk\nino+BEGlsM+xL9VRGtF8l59j5wx9qdgBsSdgn00EhBppJkHpvK5L/xKbSgQ0RfZVXZcAYSDaHjB+\n305fGl2WHrsxSqUotxsf6El/Ri9MTUgBvfdGRDwGiQWVPgrEzRn0hLVbtL/fnCF9xgYxot4V1mgr\nQXJUL6BzdwEpybWARtq+oxem6ujGSbjjPjt9qWgr1zRe5lM/g8FW/BTKhlvLhnIaIpS4/lxrPoCH\ncEYt6M/zqYmAyFF6qUiSrktIifM/s4wIIOgVEnyapip66JkZXZdYGHgZRIDa6cVeuQnpFZqt9FrQ\nq/NFiccamLug/Kekv5x4DCDemWRrRGEj18hqIKmBfuxXfEmzsWuTn5ajnrFOjbSoWKSese59jvTC\nnkJr8kgvTNV1OdMrPEjZOme4z3KMGNG5vLYHQIwT2+fDErQcmT7KFiboTlA1CedMWBNIWZE9cLdv\n64XdirbHIqxdKM9MoSNKWN/uREp9+MxiAZ20PU1H180eiJKtZtH+bFGwkdTL6Ohe03hNID3RESEC\nTgGbdBlpG/8I4XRrIImK/KcAy5OBr5+Ngl6pWrWAUCfQr+A/pq4LwPCUM7oufXFSp9NQdV3Gz8aJ\nRy0REF+KnRpIsq4LeSmWz4cw2WrcZzXQJ5Otrmp2Juk/0isisVNnwlq70C0WsHUTAf73xkk1yCQC\nepeRvK6LHx0c7jO5xkgvLCfwGneCyp2xbXSwFjtEiYVlEo+uGBHQbY9vzuARgFaLjKf5ukkzvVso\n0E481qLgivQDzHyAHt9EFDb1vOnphaWLOw+kF3a3y9gegQ5WkLJRwmenvdsRanSJxGNeI817F4iS\nrWqc6Ixvrmm8zKd+BiNyvGprcqAGWO0gdRh8yRlAN35WLFCFobeCyjPs3iuGnKpydi50OV2XhuZA\nWtcl4rfrjrJFX7BrIJmrkildl8jxZiklHW2JnK6LMWALkq0ZXRegv8+ZRICrNTnQR9flq5yd80a0\nPbbKyQaBUQv6RWwlSVXs0TMz36FVh6WT8EnNF+iFPbauyzhn54xN217/cuNEeeZ1XXwXul7COqvr\n0mtB7076Z6jRXXTwAonH3V7VdYljOo2mSKK4zKgrldp0uZb7Q40RmaT/+DlPwfvuFAIhXEIAACAA\nSURBVBc/3l0gfG8OFTHk1gvzJOHqnJmYrkktlHV0I9SoTmHrJekzOrrXNF7mUz+DsQkMIXMZ6bV8\nVXVixvlKvwW9O9ioQaUp4ZOvjvsO0E3QMl6tINIVP2mN7ctINkiN1qg68t58qq5LnTPq5KDaSiQW\nqCLhLtcytUY3Em78nC9IVXVdboIzsSbN2BFWvypaT7C/nq1ktLN6FNxxTr1lNdD/Doug6wKMfsXZ\nmryHMABmaNl0kB/WRGbSl3qRcF6KSo1H4iS9B/mR0XUZ27E7dV2ihLXo68kuS7ZCR0bXpZ6JLR3K\nQw7x6ESV92wlc6Fzo8o37vcm0vKs54McF0e2IpwPq/4zyxQ2sjmDss/MGetGPErfIRUXr+iiYJW+\niBKPqi/tIVt1Hd04Ueg8Y90J64yO7jWN1wTSEx2sUKemY9A+TLKB/uVa3l3jMvQKGcofPLM6n7fF\nNJMISMxn1HXpUdiyui7jGtvvdkZbIqxKisK7blt5WO0sTdellEgvLFflDKtVZm2JRaqcIoU0Svo7\nhTrl1uRBpbjSNdggFei3oM+1mI4pbClab1fLxqiBVJ/Z5FeythfphWnaFyRC0fbMyTPWmDQLdeFU\nTSUyCadrpEUxoi/ZKjc+IGnCGQp8fz4nGkdsTU7YSiYBbqXldO4Ch4Pe7IHp+JVLPPb3WdbybJ7Z\nCaQspeWZKeK1famaqGCkGFRfGlPYfOjgfIz4MKCBzHt4TeNlPvUzGJFh3aboXEwm1Yt2yR0mgcCr\nCP8NO9klxAJbQ65mE11KlMAgakGfqWYzKC6rWKCs61KTZv19VoU6nU5oGyUed0lB1s57o1xE6pzO\nSkuvclN1XZxVydv9kLwsGfeZEOrUE+DtFvTupNkYsDmD1EzyI9ILy3bba7/bi7S0F5MV4Xtj1kjT\n/FRsK4quC3D0pU7EI0FhS4khdxI0qaR/lPCRNdJ8l+Io2Wo/b5JxZ9QEwKuBZKawiUn/UC/MjJ65\ny9BHzXcBpnPm+Hs9qPLb1Bkb3wVS3YQ736GaqOglW3M6uu0zNqujC/T2OUdha94hTzpznn3O6Ohe\n03iZT/0MRj1sWy0YM5SzHiIgG+j31ijrAVHBC6/rMq6xp2WTzEb3kCRJkcnmPou6LrUFfaSBpO5z\nb33qfKFYoKyptDr93OR85mp25plv1qum5hOQuNAFjlfVdRnn7CVbs++N8ZIdVTl3IvUx0m1IUM6Y\napVGSQ30wpJ0rm7FL5F4bLbVTtBHe8mUYRhmCEC3falzvlzyo33GZm0P6Os+PaauC9BP+GTikagr\n0p2YVI8KHaquS9ULa9lKptNlV7Q/qety+bPvzJlElTffbVHXpc4Zozx9CCS9NXmsF6b75lXHlyZ0\nXSjb8+2zTh+NEuA5W2nZXlYbNNYLS3yHTV+qt4vv+dKsjm6v2UNGRxcwghA29U7aT1jr2sHBXUCw\nvWsarwmkJzpi/mryUhxW/DIXsPacKke5N58a6ANcO3Zlzk0UpB6Sui4m6C+AoxZEUJWUhfN8VQyG\n059CBNh1XfrVKtX2enpho66Lj6qoJm+BAD2T0nWJkyk55IdJqDN4D90t6M+aA95qthc1qr83vcpu\nBmHQ0wvbJ3RdOL0w3fbiFvQulGcu0Ad8l1h3txyg6nsF54PkSxnRfmG+Vf+9USls4xo7gqwJCttm\n5W9NPv6sZ58ZPSBF1wVAtzlDrrD6sKjyO1HvcFzjEs0Z2sUndT6ma7QUI5IaSLYzdg5ar5ts1efr\n+VLF7wFHX2rU8uzphWV1dIE+VdGZkMoV0L1n7DWNl/nUz2R0oXMnqK7H+M8BW+LA60IujbB7Ufui\nzhlR2NSuJ/0W9INMlQJ8gotAv7qU0XVh6BU55IcH4r2MrkuHXpHSdQkusQu0Jle+wzpnRDWQKzfB\npTilZdN85qRQZ5RUN2muZakG4xp7emHehLV6uWE00lwoz2yr8/FnO35FmC/WC8slmCONEy9KStN1\nWeK96Z8Px/jGZHsZXRemQKYWd7p6YSnkWie+ybQm38RnorWpxy7np9q0vZzt9fTC8hppPS2bBWzF\nlDTLoXsIWxFjzsu1TM23EouCNx3by9G5Yp1aNTlTf645n1OW4PTeeJD52c54l2t5d41ZMXcv06Z9\nt9D91DWNl/nUz2QwFzCflo0uUO12lBF/VYWgA31HmUoEnGCm767xcESY5C6I7UtxhvfsvsTGug0a\nlBjoVFrkqgORhEslHn2XYsZROjWQVF2XOudDifZn6FzhPosik4xemK7rwgRsvmq2TOciWtDrFzpv\nMqWnF5a53DCFjpyteJEfcbEogVDsfI8quqc3n5r0Bzi9ML1SPK0X9hR0XYB+cSd3oSvN6v1ijVGM\nSf8snSu6xDoTzGlEtKmwCvQLWreJQigX33j1vZyFE9XvAejrhSUKqxzK02t7agG960vTOrrGhhlh\noUPb51IK5UvdtqL40msarwmkJzz6/NWsIXhF6S5/9p055Ypfn7+q8qjrGpv81UxQebyATXG9T0Fq\nStelDSdWYat9eHKCwtZFruUocUCP6622Jg9gq9nKTUd3ZvxMxlbenXMYhuMz65Wbnkaabisd+lVS\nc80pkhtppKkUtlAvLKPrsl51NeHGz2hJuPFn22eihho9ztfSgkiese5EQEvzqfqGHA2wZyuZpFl/\nnzV0nbs1OXEmZhKZHeHdzHsT2YrU4KLjS3epS3EfYZCzFW9b7e26rT+W1XUB+tpZ1gtiUpYg0mR0\n6cLldF0ijcfEe9NJPJ7PRE8iIEVhi/TCxMYHkV7YThSoBvoFrazeIeDzpUslW/06un0/lbGVnr5X\n6oztxIiblaijS9jeK4XtdTy5ESE/VF0XKtDPIAI6l0Sr3kfiAO23Y8/znqcO0VlaFVanwcCTVXpF\n/715TF2X1apPKVGpTXWNVgpb55lTQapZ16WuMXpm6d3u0SsythJ2ZvFrpC1R5fSfDwnIeAe1kKNX\nRIkA7ZlbemGz0H+tZ05QSiL0jKzrYk62xpXdrHZWez4dxdXTC8s8c/tMnJWw7p0PicKEvwV9n8Lm\n3OdbMREQxogJXRcK8ZhJPE6scRHbWwgdrNGl3En/GPGYiUfczR7cqNHxZ6eLgvmW9kYGBiFboha0\n7vbTEh65Ltn9xGMKHRxQ6nMxYqSH+jJTKS/zqZ/J6Bn/7VFrR9Z1iZAkiuYA0RVJgRJH/NW8rksE\nufQgAup8auUZ6FFUFqKwiVTFEHaf0NPoUdis+7zXKWx9vbB8pWUqkH4Kui4A2YI+kXicpJQk57v8\n2fvjNqWn0Q+wvAiD+syefa66Ljktmx5kPKPrEp0Pmkju5c9OzmdPtnrPWPk93HSScMnW5HUtU0P1\npXHS36y5ZraVpahNsl8J/FSqKBhQ2NwaacozMy3oc63JfZokvTMxpetCaGdZdZ9O8YMH8Xh7Sjw6\ntWwOUsxZ5+xSrY16qLdz/NSE/Z3ROL7iTobC1ntvMjq6N+sVhoZemDsJV+fM2Upjn+0xor7P1zRe\nE0hPeIQZePMle/yMUSxQzPZG/NVUNprS5/AElRkUFyMyqVfoOl2R9rquC/XeKM8cXOhyKIi2FsRy\n1SpPUDlHq6JbrXrsKmeFJ08EG7cpFFesdWXVstnp1KZe0JsJzHvJ1lObbnPjAx115dW66p2J82yl\nV5V0+tLH7xbqtpVKKbGig7vPPMNWJn1zRTzq1XGXrktdoxX9t4lRXBk6l7sFfR8RoCdbowudC+Xp\nRhvXv9dtJW6oozd7aOiFmePi8e8HudV5txlMtgNiUOjI+FIX4jEqdGTRwVZb6aDAsw0zxp9t33+c\nfsUfI74ikF7HEx2RIciVmw69wl2hq7ouKW2JXgbeDIMFkhW6iaDy3OEs8R12HJuq8B8FWJkqRotS\ncroU24U1vYKsS1SrXLD2eZUbp620LyNzLnT9AMtb5UydD90q5yPD5LtonJzWTms+YDzXcrYSJD9M\niIDlqpyJhHU30H/cKmevmr3bHzAMeueYbjU7iSRpJmcSl+LePp/Rwc4kXOKM7SVbd5pANfDwov1Z\nxFDPl7oviEBOcL6XeEzN1xG9ziWs28+cKQqOP9tDBxtR5buML43QwU5UeQKB1EtYZzogLpJ49DZG\n6Sfp87bXKyR4fWk2Rozem5eZSnmZT/1MxqYr1Jngw67a/NUaYL0R5tx0HO/uqOvi5a96s8f1Uvxm\nvZbWN/5sp8qZ+A6d+xxVq9T5Np1D3l25Gf9eEwYGxgO8V63abvg9rmsMK7vKPq/c32FcKXZ3lrpZ\ni+KDnWTrufrF78uGeW+E+YBII+0gnYfjGr2IgE1HIP6kSyWsMWpBf5s4bzarHv0qp4EEtJ5Zr5r2\nzq/693p3z75emGp7G+JSLH2HRKU498xtP/XG7OvVNW46Z2JmvlADKeNLV32/ktmTVgv61Huzar83\nwzCkfHPkS/XvsFdk9O5zDsUVJ6wzMVjP9jLI/Pqz98dt6plj9IzqS/3vTR9tDGTfm3YxORMjPlgX\n6v1ImXUVoDLPfE48+uLYvi/Vz69tR8sz88zXNF7mUz+T0bvQvU0K8bX4qxlD6FU5bxOXm7rG1gH6\nNhGw3XQcb2aNPQ2kzHwRvSITpEaIANWRdykls5753X2pSCc1ETBWdtuHvDsJN37GU9nNzBe1oLe/\nN5nzhnnmVNKsbc/OACszH9NC1nU+ZIOXLiJglzgfulp9e6xXRdR1aVfwb/f74+/0JawzvjRKPGb3\npCdO6qLl5H2z11a6KK4ZtjK1xrfm+YC8L3Wf2a015vxUfEHUn7nTgj7pV3oaJ0Bynyf8yiLvTTJ2\nd8eIrTW6ba8KSud8qdPXt+d7ay6EnmxF2OdILyz93gS2lyoKdt4b5ZmjLtTZfW4h4XLfYXwXUM/E\naxkv86mfyYiMX87od4x1XpDamS+Jkpoa42GiI0mcF7oeIsAd6ANJREAnCZc9QMc1ehKPPfTMvMRj\na5/3cnV800EY5BKP3mRrKQU3HeTHEsnWjC0DrQBLTwTU73Bqn7NBahe5NiMR0JoPEJ9507G9xHkz\nrrGtF5Z/Zm/QC/gTAU5fOqK4fLay7Z2xKT/VRge/TdgeEBcm9POhP59eHfcmWyN0cC754S109OhX\n7uRH9rzpIQLcSfq3iTX2kPSZC2KkF2YvTCTP7PqzU+sDtGfuNdTJshE2vcRjBnXV0Qs7Jz8URPQy\ncWw32Wo8s3MxYjvZOsdPOd/tXvH3beIOuVmtsDsMOJiAF9c0XuZTP5OxVLXKjghwVse7wnnmyk2m\nOt5xlJlLcf391up4VPFLJx6XT5plHW+vE5S7WjWvQuepco5z9qlIGcj4k66OMxW/xLvtTbb29MLm\nVMc9F8T6efcZ67Y9wJdMYdotexPWXlvJVMe7ov2JSnFdY/eZncmURHWcSbYqz1zpXN0K/lNJtnYu\nscqZeErSWy/F/qSZEx3MiPZbzwdzkj4XI3bO2BlFwSlfOiuZYkx+PCg6eIFnziOs27anx4hxsjVH\ntX53vjQbwZxsPfnSgy+pfi3jZT71Mxk36zLZwQjIZ2YBP3/VWa2KjV8XV94tgMaZgkjWC53eKaF0\n1+hOBGQ6OQAPA3c+VccTQnduW2m1oE+1fK22N+WEnkiydbteTa6vrjGfeDRRA46fnbKV7HmzXRfs\nnAFbhwKSqY5XW9l1gpenkCi8M9petzCR+g6P3QCN1fGIwpY9Y3vvtqTVR5zZalek0Jcaq+1Zij4w\nvc8ZP7Va+fXCujSaJMIacCYC6nnjq7YvkTTr254oKE35FVFbb93rxnlIUPT7tqcXTgj0TCIJ57I9\nIE5YZ87YpoSHuTCR6TJbP+/1pd67wLYXj2QKHUQSTu7et+7Zis5G6DIwku/2tYyX+dTPZETq70sk\nArQOQd7gpf7+XlckJ38100K2L7iYFye1ttXuOI2MEB9VrUoEG1PVqvx3GHS3SSSkgHYgvd0kueMT\n9nyXTH7EoteZC52zW07cQSyTCOgJ2Lu722QDLLcg67StzAhSO0KdmX1uB6mDVTsrcz709MJO3+Fj\nX4q7z6wH0j29sIyAfZ2z70t1W3ELSgPBd2g6Y2t1PPPMXd+c6DhUf/ad+RIxXX8+vU03QCRbk0m4\nVjMYZ1x8fm+MLej3h8SluN+cYZFnNhUFs7YX2UqmcNJb46pAZCP4GRhL6IU57wI9Cltmn7uUuES8\nBFRUecdPJRJS4xp97/a1jFlPXUr51lLKXy+l/PLx/7+l8bl9KeWLx/994eLvv7uU8rdLKV8upfzl\nUsp2znqubSxB5wIalZa9Xs1eAoG0XZfJ7HadM3Pgtfircyp+TkRAy1Hu9gcchmzFz1vFAIwIpE0b\nETCrWtXVddErfuMap6sOahVjiWSrXWSyJ4Zs1rJJvTeM/liq255TR6r/3mzEdstd7ax0kt4thtyh\nsM1IWE/7qf0HPsOMUZx0ep/n6Eg1q5xudPAcWk4Pdv8E9DSaqNEnQH0c55ymgNw9EdvrUhUzCKSa\nyJw8b+ZoZ3XQM0lf2ivuaOvz0nLqnFMx4jAMuWdeADUKPIweapqiv+kj8637vFRcbEz4LCJEbix0\nZBDWVS/Me59aKI7trFFNMF/LmJs2+xEAPzMMw8cB/Mzxv6fG14dh+OTxfz9w8fd/FsBPDMPwuwH8\ncwA/PHM9VzX8F8S28c+hBji1bBbTYWkYf7aKMYWSmiecZ0wsdBIBOWpArytSIkilHK+xHfsMp9Gy\nlScRbDQc5eEwYHfwtlueYyvTz6xfRnqUkkVEKxcIKp2UuCVowm8zz7zp+an9oycegfYFbCl6hddW\nxur4JlXZ9SYC2nph+9QzN/XC5iBljcnWll/JXJaAJc8bTyKghzbOP3NfvNhNcc3rAXX22YSkn4NS\n93aZ9T4zEyNaqdb7XOdMoE3be2wdKSBOtro7iC1BL8/ss7/LrDFJ3zsTE2yEaxpzE0ifBvD5458/\nD+AH2R8s4zf+BwD8VObnX8LoGX9ORK5v/Gp1vHvJnmP8Ru54dMhnkjPjfJ3AXM7qB5di4wXxKYho\nnzsEuQUXfUiSyFaeRpWzcSmehWjyJ28nk63mACt7Ke62oE9Uih+lypm6FDcEpVPPvGrrhT0ZW5ne\n57QQ+WKIANN700E8ZhMB3UYFqTV6EwFu/TGgc8ZmL0sbv2g/EMkSmNt0uxMBRhrNErYH+FDl8+Li\nBVAVpmfutaCfE8d2bcWYbJ2nP7Z8MmUeG8GLaAIWKO64C/LdZKvKRujrhanx0jWNuU/+7cMw/Orx\nz/8UwLc3PvehUsr7pZSfL6XUJNHvAPD/DsOwO/73rwD4jpnruarxbBxvz/gTEMlWtertPscdH9c4\njYJYJGBzZeAXOEAzOlKRWKBaHS+lhAGWrmXTSwTkquNAmy/v1MZx64WluePuKmfnQpfRQBrXON2C\n3n1BHNc4h1LSOGPTSDhn977pZ85Wx2uy1UcpaVeK840K+udN6lJs1AuLkK1p1Gh3n1VdFy81INIL\nS3+H9uSHj8LmTzz2EtaDXB3vtaB3o/+A0cad6OAsgnJcy7SmEpDRCwsQj4nk6BIorq54sTnZ6rYV\np+5TDjUao3FceoJzC26t4o71O8yusYHynKNb15SzMCdbMzHiNY1N9IFSyt8A8K9P/NOPXv7HMAxD\nKWU6ogK+axiGr5ZSfheAny2l/AKAf6EstJTyWQCfBYCPfexjyo8+29EzhEwiIBKZVA2114L+lAgw\n8VeHYcDdPlEd76BnZgmqNQI2IFnZ7R3ISUTA1HBX6DLvzTjntFjgEpSSjFh6JO7nvBS7+fJzvsNK\nKbkvJnm3P+Bf/VDoLt6Zr/7s/XFOBOidoPrJj4yIdrvK6Q6wnInHu/T5MJ2EmxOw1Z+//7MZAfte\nEq7+XaqyaxL+BPrJlMyluI9sTZw3jF5Y4lL8G9/YvfP3+8OAwzDnvWkhAnyFjvy7PX1xz1+ye0Lk\nM84HEy1nXOO0Lz03KjAnW40oiFxCqo8aBaDHiY3z4fQeOpMpCQH7HkLxbn84xfbSnEGy1Wkrbq2r\n0fZ0oXRgmraX10hrxDczBOzH9QzvxEa3+wM+/CYX0036UvOZmNbR7RU69non76gwkbn/XMsI355h\nGD7V+rdSyj8rpXx0GIZfLaV8FMCvNeb46vH/v1JK+ZsAvgfA/wLgt5dSNkcU0ncC+GpnHZ8D8DkA\neO+991qJqqsaLaoBsAxsNZNJXQK2OjXfqd1ylkbTWKMTEeBGDOUrN+OeDMPwTvXR7XgzgT7QSX4k\nEQH9AMuvF5YVH/SKaJtt7+ISu1598HK5BL1iu9a543FlNyOi7U+2TmpBzKlympOtv/n23UTAnPnG\nNQ7AvTYYb/cH/LbtTW4+a1BpRgT03ps5CWbTebOUXpjb7wFOmjDx3pioQ0voc6SEyLt+ZZ/yzU1K\nSVY7q2F7h8MwXmxn+Kl317gQLSd1PjhF+1c4HFvQ30/sZBDWD5l4XESfcFaC2XPeRMh8wCfh8Tat\nW3e2lfs/e7s7YPvN3kJHKWeJCn6N0++NG2ENzEXKepg71zTmPvkXAHzm+OfPAPir9z9QSvmWUsqb\n45+/DcDvB/ClYcTU/RyAP9z7+Zc8/B07+hoBSzgNF2R8DgwW8Bl/5HiBzDP7YavDMdiYWmM6EdAI\npNXnrXP2A6wEZHzC8VZEja6dFdjKErouTwAyDvgvdK19ziWsl0m2To05yVbXd9hrQe/u3jcHgn75\n8x+Y044I2MtafXXOh6JXzOne5xLtr3Na9cLM500kvLsEpcQloj3vzG5QSlLP7E08AgSy1YXimhHf\nAEbtrAdMPM65FDfXuETCOpV4nEaVv53hV6Zih2EYRltJx7FmVPlUjJhFBzckPE53C3cRbwHbU4uC\nzXhkCQ0k9zMngRfXMuY++Y8D+IOllF8G8Knjf6OU8l4p5c8fP/N7ALxfSvl7GBNGPz4Mw5eO//Yn\nAfyJUsqXMWoi/YWZ67mqsVm1W9BnDGETwNqziQCn49006BVZyPimlwjY64Jqvfnya5ymKmYDts3p\nwJuGE2c0mlrzpYPURnX8DPHWnNDGfEHs2UqKPkq1oE+8N0Z9jlo5clFKeuiZLHd807yM5C7Fm0ag\nP1JmE9Xxnq0knrmUgpuVt5od2or8bvfORK/+WBYyvln5/ZS7Ww7grXK2fOmZDuE5Y/PV8f4Zm/Gj\nQDshlamOjyguZxOAYJ/VZ161k613e70TZ52zRzvO0Cl7vl49H6pvbuqFJWx5XI93n3vnTTY56krS\nb7pJ+ux5Y048Nt7DykbI3gVc32G3uGN+5jkxItA6H/JnrKuwOs45nXjMUvRbMSKQe7c3nTj2dqfH\niNc0NALkvTEMw68D+L6Jv38fwB87/vlvAfh9jZ//CoDvnbOGax71Rb/dH/Ch+5SSGRnzVrIiS2Fr\nBWxArsq5RLWq+cxZCtvkM+9T3PHteoXfvHVSSs6V3W/afvC9ySDNzsHLfnKNWQqbM2nW0gtLf4cd\nfvvt7oBv3mpH5+r4XjjXuHULSveeeUZ13BWwAcfzwf3ePOHzZpzT+960bGVudbz13jwJP7VZnZ7v\ncqSr4+tpvbBaHX/s7xBo+9I5z9x7D7PVcdf5sCXmy1Bm3bZX1zhJKUkgpC7XM3e+usbuPsu+1Oz3\nNv3kx4dVrb5jQmrqfJjjV/7lhF7YYn4liTZu+YG0nzKzEbzx0sPbiu2MTd+nOijPOUh6OxvBu89T\nEh5ZNkK1vbeN8+YVgfQ6nuSoxng/21ur42r2+M1NDV4ayJSE03izWVuF896Y4dP1O2xl4HVBtXbV\nIYNYqGvsCpHL+7z+wM9/YM4E0qzON9mlJP3eeBEBb26CRIBYbe+9N1mn0Xrm+ndqdXy0vc57o36H\nRzSeCxHQOr/qGlWUWZ2zK2AvzjnO9y6lZO554xScf3MT7LO8xnXzPAQSz3zTeeaEiHb/vcmhKpq2\nl97naVvZV60+03z179JnbDfhowXS7fPriGhK2B7QoJTMSKY0KSpJ23PSjs/PvPwZ+zaJ1mv50qyI\nditGnHN+jeuZ3hd1vtWq3xV2jPn0d3sS/TcjLr78+Q/OmbgLnOabblSQO2PXk6iw9DPf9OOlTIx4\n+fOXI9PgAjjus3mNThHts614ENF1vsk7ZPa8Ce4/+hpHCY/7XWGz7IbemZ3p5H1N4+U++TMY1XDu\nV0bmCA0DwNu7CSRJtsq5WeHtbhqZUv9dnu/OmIGv3+HUnIlKS21B36pWpb/DxvrqvyujVoLv70u2\nOr5tzFfXmH9vfNXxN8c9eScRMPe9MSJJes+83SS444HtqYiA8zM39jk7n8n26pxdW1G1rlpn7Izz\na5zPaCvr4IxN7Eu32p4+HzzJ1iXemzetZzb7lfnnzbv7nEUE2Pd5Mdvz2Mr6KBzutz3ndzh+R/fn\nPBwG7A76xb3rp2Yg16afOamdFcWITr8yJ461+qm1FSkbnolWP5W7FNvvAus1dkfUiGs+oB3TZdAz\nTb+yy3Wya9peVrfO7Ffe9N6bBWwPmGEr977HLDL/Te+MTfqVaxkv98mfwWhBLpdA47gpJVlj3TYy\n+lkYbPjMzuTHjPmcz9yCte+S1fGlgtQ+HSJ3GblfaXlKcOfeM6vJnmg+wJwImEOvaPLltT2uczrh\nzq3zYYnE46zzoWF7N2tdULpVHV8u2SrqzB21JVrov0yg76Y+np75Hq13buLRet609nlOAslJNeg9\n84zLiPM7HG1l+rJUf5+6vroey3xdP7VP+RX7+bAeUZ73tTxn++YGpT7lSzcrK0Xfn3ic9lPpouBD\nnjdmW5lzt5iar67RGtMlEY+t7zD9zEEcmwUhOCn6283a61eCe7PrPgXk35trGS/3yZ/BqJBLV3W8\nwg9dVYy6Rid3/M1mjf1hwO7+he44XxbC6URdvdmsrciUN60M/AwIJ/Due3OGcPoy8Nnq+Pgdvrsn\neZhprS41LnTiJfZDDdsb16hDxoG+rThtby7E21Yd7yAesyLarfcmWx0/vdv3clo3WQAAIABJREFU\n7K9Cxq3VqhnUR/t5Y0QEtGzvtEbxmUspnWfe220P8L03eT/VT8LlquPtM3ZVIFfH3xxRFe+iPGfa\nnv3dXt720nTPRgU/XR2/adveHCqS9Xw4SSd4kmYnP+WMYxsxWNpPNc6brM5cK3afzUZ4ID8FzDkf\n7sV0s+NiD8IaGO2v9cwqdXtcY3s+wBfTATnK7GpVcLMu3jh2s2rezwDfmZh/b9rItbsXLqL9cp/8\nGYx66bVVq0JEgLHSkqyOt9a4xDNnquNAv0K3BALJVWlJz7cEGieqjrufWQ6wpm0PmGcrD4VoAhKO\nslW5SdpeKaWLWrCirsznw1zIuBVpZkZV1Pmauk+mZ85Wx4EHtJWZl5H77032grgYGses4VZ//nLM\nRQRYka09W1kCEZDc51ZR0I4IWMA3qxfjVrJiPgLpYWKw/HnTvhSnURAtxKP4bm/Wq25Tj2zy1s1G\nqOu5vz7nfPXvnPs8C2HdsT3X+bDbH3AYdNurcz7o/ceMQMr7Zh8I4VrGy33yZzBaGfOsoNopeGlV\nWowZ+DmCalNrzIuddqpVM4R8Jyt+M9A4UwdyHjE0nTHPHqBVZLIpRO6sFO/G6vgmuc+txKMs5Hsz\nbXvjGvXW5OMaG7aSFlfu67qkRfZbCSQzui5lezctcdJca/IWQrF+r2nEo3OfOyiI7HyHCZHJvKjt\n9AWx7okqYA9Uv+JFVfQvxR6/km1N3kPjzHpvzDpS9ec/uL6sgH2AXMv6UrMQuRdhPe2nskLDtamH\nFwXRSjwOqU52TUTAXNTVVBw7I+6cmu+t2Tff2c+bnN+rc05r2QxyzDnO10eVu9C8aYR1p5jsjsHu\nklTranv3iztpAXszGqfO2fKlVj81E6H4rp/yxjf1d2Se+VrGy33yZzDafPlcdbyKTE5yvZfgACez\n0fXnL0dWXLnLHXcjApYSIjdVWrJV09MajZDxJb5DoE0psWpLuPc5bXvrU2vSy5Hny3sTj0DnMpJF\nBDQQj7OrnA3KbFpz7YEQAbPOWBMKovXMc4LUbqMCp8DrbESAJ9ka6o+ldV2mbSVbHZ9aY5Ym3Hpv\nZlXHG/ts1x+bi0Ay+ana1KPtp3L7bKWHmZGtJ1t5KARS5lK8nk4EzNaZa/gpN9rFed7c7g7YrPJs\nBHuDC2cc60bKtp45i4iO0DjpGGzal85Jmr0zXzqOjfyU5z5Vf8crAul1PMnRCrCylyXA7zRGRIC3\nglh//v58QB4R0K6aeg+8bCVoSmQyz5evl5t7yLWZSBJn4rHH9c5VMWp70ekgVb4UN6oYQNVASlZ2\nzbY3tcYsCsINGR/X0EbXLUFRybRbrj//wfXlzoclKCVvGiKTb9OJx2Cfk4hH63vTQJq93R+S1XEv\nZdbtpyrd0/vetFuJp1Bh0flgQgRkq+11TrftWdt0R4nH5D4/ROLxdr9PIhb6fsp1oTschhGhaPZT\nKdH+zXQr8dnINWNxp4UqzyOs/bZXf/7+fMCMu4DRl7b81Jy7wNQa02dsS0c3yW6oc3qpj23bKxmt\nvpvpQmj2PrVZFawaTT2y58O1jJf75M9guPny9WesAmi9SvGsDPw03Dl7KXZCOJdA41yu6f4a089s\najFdf8bejr1RNc0GbECnsqt2dWtUMSriJ6Od1XrmOZpK4897OkE1RSvnItea50NGf6wjYO+s+CWf\n+Swy6W5z6xX+BNpBZbqy2/wOE7ayCAJpieq4z1betCq76Xf7gWxlbotpExqn/ozbr/SaeqQF5+88\ndK5xjY19niOG7PT1Zg0kt7hyXaPT9twyAi0aTdb2gCP9qkEdctte9j0EJgTnk/tcEzr3bW8WG6Gj\nATvrmZtxrBeNY33mGX6lR2NWi4LuuPik5Wl85msZL/fJn8FwX27qzzwEvWJuddwuuGisjjepAVnI\nePDMWa2rZhXDDE/OV8d9l+LzM3ucxma9GqsORttzV+hCxJALgTQzMG+KTFrpo7nqePN8qIF5MsBy\nVsd7tpdtaV9//v58l/+urG/8eWMypWkruer4drPCboLuOYe6UH/+/nz192XW6Pal1vOm1dRj7nnz\nDl09fyleIvE4tcbbfU6rr/nMC/mVrK1YBaUbfiUtRB5Q4tLP/BC+OU3bm7a9WWyEdbvBhcvvzZov\nim+SKM/7zzyHjeBmdLjPh0WQa80YbJ6fuk/3nEM7BsznQ48m/KqB9Dqe4mi2Jk+KgdU5213TktUq\n4wEaQS4zVYcpkclTciZZXXK3AgWmM+brVUm0W/ZXdntVSft746zcHC8j+Zaqzktx472ZIUQO+BKP\n7vmAvtC3Krpb13i7P7xD90xDxgOB19wzv3vGzqaoOG2vIRCfPROj6rjL9sY5zXSIGeKpwARM3kxF\nqtXxrC/1fofBe5PtztXwU5nqeL+tthcR4J4PyCUCmmLI2TNxs2621c7OB/g0SWpTD6uf6qBnZu3z\nffRMXeNKPGODMzvnS9dtv5JscLGI7bXQMyZfOv8+tQBy7T5CMS1E7kUbj3P697nV1CO7x4A7jn13\nnysb4VVE+3U8ydHW5/Bmj4dhSHe3aWWjsxonLcHFLF++JTI5Rw+olY1Od17oPPOcKkZbcNFYlZxB\nDZgUmZxZdXinurTzVpdmVccb3+EcPSBgOjgoZeRuZ+bzI5CWr9Clz6+G7c0SnDefNy3NtfR700I8\nmjVJbo+250QE3O3yKK6pNc79Dt+Zbw4iYOKZa1DtFJy3IwKOFfw3Iq331ErchBoF+r55VjV7AhHg\nnM8d09XfYfX1uyGHsF7Cr0w88yLfodlW7o5JOJky24wR6/mQFNGe9CtJiv6xqcc7dM+ZsgTOfZ7y\npXdz4+Km7c2wFVMc60Zx1TkXOWMn1ujtFjrPrziZQNcyXu6TP4MRZVJdCvpzoH1+NE6QPTZlzGdB\nxruVllzlpv78/fmWqGJkKy1Ticc51exhOAdAdcxptwxMJALMlZZZrcnNQWoPoZjijjeCl3mIgHab\n2zkoiKk1Os+bus/ZwNeOXGsgAqyJx6RfqSKTD1Ll3P//7X1rsGXHVd7X53XPuaN778zoMZoZyZJs\ny5ZlG8lm/HbAFjY2JkY2EcYmGAF2maRMAcUjmPADEgiFiyqcpCChXLwcCjAuAmWHQIwxDvxIAAsw\n+BUjGSNbkq3RY+beuffc89jndH7s3Xv37t37jGav72juY31VKs3cGS117+7Va/Va31rdMDueB5ir\njAAqa1Q05+oZK9o3C9h/zDOWzWwVNdFe9KiHyJZWLyNLKR8l6YqoV187/qgHm7kmDbbWnrFkZj77\njF2GX9x0jLW99RqeN0CNbZboHpVpFjljl9bAnvhQwWzWqBqhV6d7kibadY96CAM+VR+s6YMZy3nU\ng+nfHBQc3pnvA7Brx4EaRRAa8rlFtMkks8+C7OW5SEmJgAbL7y1RzwhgX7L9P7/UMYZGKJlbWNt8\n30THKHZS64IfnGduxQ4bcd8sutCJ9mFdg9c9oCt1JSCNnyYn91xzY2S+slTPCCC/dNkwO56/IFbb\nc439xLTgCfpI6dBe6CPlxshuyj1JlsDyJJ+JrBeHANQ/6iFmIFXXZa8EHtlsnPpAwKxZ090lMJBi\nyZi9ZZvrL7FMxqOoiXZE9yRJwUW9qai6J9QV9v2Hzf4DeIHHdsug0zLk+1R94LFZwm2BrkgqMJjn\nQ8SPVQaSBpD2NC4WgW8eCOBelqJjbBoIuFitN6lHgMh5WdjXReCwRQ550asnxFfYYk93SlkV6RhJ\njICLZR1I2SXJpZj/zG29oWxyNtQ1mRSzZwLdm88tEsFLdgAx8Fib2RU00Y6wKuTn1zICAZxLcTrG\nKkuKrXv5GNnMNeq+2TuBRxesiM25KdsYiPe6apIdT2XGepLIXpaKnbHS7PgoYkupl2wxeybea4ea\n0FqCnWo8xsj5IG1EXsekF70KGztjG8hz5Z50v7jGR2x0Piw4E0XMFHIvz5ChKNs3C166JPuxTQMV\nC/3YPRB4XNRbT5ZY5bzeB8R76+W6oj2QFHsRF8vcNG0GxpS3qH61kbwFWYwm2XFgcbaq2Rir39DJ\nbFo7DvAcNveUOPUVNnLTykXlUuxLcTrGS983i5hr1Cxnw5IStu4B/CaTURZXTp8WNGStBCtmzSjj\ndS9LiSje9ayKJvJ67bTcM2wyKb0UR/dNQyc1yiQRNNGO7RuKkxrZ2xI7xaS1Lwo8sm0p+4xtssZO\nZu2LQ+y+TyIWBOeMveijHk3P2FpbT3xYoGET7UV2Crj0Xn1AjU8n7TNHtM2LEqtNbXOMBVEEP5rZ\n0lo71WSdFwRHmT6imD3DZiAto5dnVPcEtplJQojYKVeNQPVj90iJPuDYvHWPjjSzfQcBGkDaw8jp\nhwGFU9T0LdbgVdBoOI+YkxkB1TFyD1BRU9uaLGfj/hx1c27YRBuI13qL6p4jgUdpM0OAN+dF8po8\nt+xk1jECmr5GM6t5SlzCCIh+Q0G2itngle3o1zmVTZto1+ueoHlxzEkVnjfxMXLZOLLzJuaYy+xK\nbI2BhpfiGjsl/obEQMCi80bCCIjtG2YPpLTpLtNOuX3TLPgRs83LsCtN9k3dox6yPpTkErbaBvEN\nz1gX9K/s7VTepfbqA2p8OgKrIgwETBsG6dmXYiDud4qbaBPtXhF4XO4ZK36QYhl+bMSucPvM2UZn\nQyqz/oxt3EeK7CMC8YAP104JxhjVPbcPL133Dgo0gLTHEc1Kipu+hdFt2ctSQE1vCXJmd69QOHvt\n6lPi7rllyZyjfRYEjAAm3TmW2RXR5BcYDVGmmLhvYrXeUgYSc4y131AQCFiY5SRn/Ni9Z0TlnnXP\nLZOZa5I5s87ERWe27LzhXYqjuidk9wC8feMCAew5M0ubFpYiSRzzCCOgSf8xN8Z6O9Wsp1k4vmQ2\nx9xyLzdNX2EDLsKCIDFbl2enJP3HquvctPxjke41nXPsUQ9pqSJL95xMth9b33NN0k8w0paA7CN2\nWs2qERYxotnBD7pPx7wLSHtnkVlcQHzOTexop91Cy8RZo43HSNa9g4LDO/N9gsX0ZE6WU3pBBHjB\nD7aj72TWU385LIgia0q80EmMxkK6M5dVQQ0E7CGHjc0IqHOwGgc/yLoH1NR6C+nOtc6GgFXBLPeM\nNZl08ppkx/kNXrlNJuvYOGPyeSMKBJDt3qLyK4ljznWk+SWzvgxfJj1ZtIeCKeFT4qLAQl2Jq8iu\n1PfTYLHKC8Zjs159wPIDj5PZTJAUXPBiIYnZOp/bxszW/oKXdSW6UpXH7Rcmss3sBBm5F5eTyS5h\n82U4NLWl9XOeye4/xL6WC5PTe+jOF1vnptUI7DvkQcHhnfk+QbQniSg7Xi+vKX0a4DWZrH2aXHIp\njjSZlFLG0zFynFT20+RAvNG3nCZPbOBY12eh4Zxdk8nYId80O75I9xo55hGnkpIdj75IJgkEkJ+g\nJ2dNfRm5zIbZ8XSMcWejcXY8mvFjZDm5QfoYI6D5ecNd55VO9SlxSXa87nIj0ZUom1eQHV9Y7imy\npZwLGLtcPZUZs1Oy7Hg4RpmdqtE9aZkw8RK78GVd4oWuMZOkHbdTS2NYS3TFOxMZgUdm8COuK1IG\nEl/3WLY0f9SDqnsLAo+kOUuqEerKjsX7hvyoxyQpl3tKG5GnMrh3PmY1wuIHdQ5vGOXwznyfIBY9\nHmclKo2z47WN8ziZGyezaZNJf0wObs5NsCgCz6qxZXzD2CHfuKntAkZA0zlTG38uyC5J5hwPSPEa\nvOZzbtCfIzZnSgN7ku4BiwOPkgavvrMhfQQAWEIj34gj3XQf0pv2R84b95Kd6PyKBaybzjkaeJQ1\n0Q7HKE2cAORyz5p1lvTqq38EgJwdp9qpGVf3GOs8jewbZsBaxECKr7MxaPSSXexRD0kJbn3gsdlD\nBYse9RA99kC2U74M/9fMvnWyZsj1QfpG6xxhIBXVCNxm6Y3XucaPZZ6xY/IZK+ovVBd4nO2thwrm\nwaMe0gb2AFtX4mcstRF5HrzVJtqKPYoYe6ZpM0MgzkCSlF/VsSAkTSZjzsGUHIGXNX2rXmIp8mJN\nJomBgOlsLnpuOQwESJvSAdwLXV2jOyZzTdZ8veocyBrY1+teY5p8xGGTNicFgqCZ0HnxZeQyBXOu\nPWOJlxtp40+Ax3isCwSImmhHzxv54wylQADhjOWeD/F13isMgxhzzVrbeIy1j3o0LPNJxxgr5+I+\n6pEzb0UMJN46xy4jTvcaJQW71cuSxDbXBQLE6xzxO5dRPipilU992yxj9wDVRIf8fAjZOILHHtpt\nJMGjHpQzm3km1iS0RG0Jamyz5CET2l2g9hEhie6R73yRMS6jibb4zrd0H9H5dNpEW7FHEa9vb147\nHmVVkCnjee04s35V6rAtoZ9GjIFEfbEjmVEDAdKa4vAp8aWUIiXC4AeRVbGwrwtpzmNCA/toSQk5\n02JM8+eWwzFSgh/kvc2kOy+j/5gvw40PaOb0dlrpU+JL1xXGOnvBCskFcVl9Fqj7ZsGlmGVX3HPL\nbNaV7EIXZ12tNOyd5cvwf822U0x/RFp2XFfuKdKVsLee2AeLNFcm+4juz5rI82WU5FF9Oj7jsWng\ncbEfy9G9fIxkH0zUR4rZDyhyPizjvBkLSvR7nXY1Ob2sOTN1ZQm2lG3r3Z8dVhzeme8TxCLw46ks\nkho2mXTRaVEEPlI77iLVTWRy51yNwLtosmOFXKq8VIb3DSnyqg4WM2I+TuaiNXEyfHnpn126zH4k\ns2utlc25G1/nlaa9cWK659a5SR+WSD8Nie4VvSWCdZ5K1jn+2t5Kp3nJrBuTP770z3gNXsfJrJHu\nOZlReY33YVz33P/r0sdXzXJK9k0dy1Ose5UzewZjhKyraeSMFe0b7plYkSe0zeMk3lui32CMcTsl\ntc11usLVvaZjjK2zSPdqSmZltjlmp+YiOwWUgx/FnAW64o0xmc0xm1vZOsdsM9lHdP+vJvJ8GSV5\nJN1zv98zdiqqK1zdS38vsSuRdRYxU6pMOJGuRPxYiTxX7lm1K7J1rviIyQydptUIESa9xK7UMdcY\ntrQkbwm65/7ssOLwznyfINbobpTM0W+oCLFMyyhThCYyo/Kyw6ovcDbCaO8omYnmHGYdXNPvPimb\nLZJXk3UYTZsfeLGI+Wg6a7wm8TkL9k3kdZvCCAmehI6sc5PLl5MXWxOg4YUuxkAS6F7eZDKccyJb\n5+i+Wcp509xJ9cdorU3XmZitSvdN831YySBKdGWB7kl0hbrOMXmEwGPZrjR3UuuYa9K9XbErAjvl\n5uw/JZ6vs+T5dNKZ7WTGdYWre4CMPcOac71t5u6b8XQmslNA3Zw5bJyRIJDpZFbnPBf6iOVLMceP\nLWTm8hqMMX9KPOqDSdh6XB8RqPNjOXYqmc2RzK3QrgTrLNS9qo8o0JXI+SCR52TGGMzs+49kTQDi\nXSDfN1V95topQUVHp4VkXmZ5FncBLWFT7FGwFSF2AZNciuOHiUyx6g68ptmvWJPJ0TSNwDd90hGo\nCZo1mHORdSA6lZGIudTRB8rZJYrD5gdTxPumml1ahhFqOsb45aZ54BFAtMmkxKmMsSrYgUdJMGUh\nE4563jTXvZVO1mQyEvxgBc0kgUcgntldhq5QnVTJeRO5ZIsDjzW6Jw8E+Ewzia7wkztxu8IPpjQN\nPC46Y5sEHuse9ZBcwKJnLCHwGA0wkxhI4sBjNMAsO2PrkoIiNi/Rj13ptGvsyt4IPMZYUnsy8Bhh\npkjl+SxPth8rkedkRs9Ypo8o0L3oXYARePT2zSxrg9I4wBx5UIeeCBUGCg8CDu/M9wliVDyGIkSN\nBonCKVWsKOVSYigjTSalDmA6JrKzQbzQxbNVbCdVfimOB6S4TmVz2n21yeQomaHdMg1fv6qyrqSO\neax0SBL8YDMMYuVXEl1ZRuAxVl6xtDOWVDIrDTzGXiyUrjMz8BgtDZDYqWgQThZ4rNU9aSkS7Yyt\nlpdLgilOJttOMXUvaqeELM+wfMFam7E890bgMXrGJvIzdsxkldfaKdklu1TuKQg8LvJjm+pKL+LH\n0s9YYdAfWIKdIgYeY6VDlGBrjNkqKX2M+jfEM1ZyJtbonqQcFeDd+Rbvm+Z3PmbgcRl3voMADSDt\nccSougxFYDGG2JliYBEjoHn2K2wyyYjAl+jOUtpqcHGXRuCjWQxBDfDCEjYS3ZmRHa/qCmGdA12R\nlEoBceeFWy4lCZrxdQ+oy44LspzR86bpulSbTMoCj/ExNg08sstHgbiDJXEq6aVNkRJXiWNujKk0\n2RczHpdQBgjwmK1spqwbI5vZGi+v4DGsJYFHN8awgb21ssDjssvLx4LgxyIfsXEwpY4pK2DSh496\nLMtOiZJ43t7OA4/U84Z8F0iar/My2lmwWzHUnQ9cxiNhzktuxSBlcQE8uxJ71IPCeCSWl8ce9XBj\nbNqY+yBANHNjzHFjzEeMMfdm/z4W+TuvNMZ8wvtnZIx5Q/Znv26M+YL3Z7dLxnMQwW5aGacfNr/Q\nxZpMSpoZuv+uWooka/AKoHJ5EEfg/Wi0oMGrk8lqZujGEc86SLOcsQy+hIHEc1LrGwNLMy1lXZE0\n4ovJ8//skmUGpUizucVkJnMOlqF7rCyne0o8/g0lLIhqZrd55rmqK7LL0gLdEzGQIoHHvdJcOdJw\nXtr3KbQr0iaY0TJAyTp3q3ZlnMzREr6AyJ1zlbk2FmWzq496jERNueO6B/BsqYSxANQF4SQNpcms\n8oV2ilwaTWXPNNe92KMe4nUOzps88CjSPeIZG1nnsWCdY496LKNp/0hop/xxuV/LG5HzmGvhXSB9\nXEZiS+O6Jz5jpxx/JMbyHAn9m9iDOozWL+Gdr9dpodXANh8USENn7wLwUWvtzQA+mv2+BGvtx6y1\nt1trbwdwB4AhgD/2/sqPuD+31n5COJ4DB35mN3LgJTP02s0UYRmZm2UwkIDqnCXUZKCOScJhIIl7\n49SycfZG6WOsyeRSmq/vRQYSk60XNJmU9sapazIpadYMxLOcdF3ZIw1e6xhD4vr7yIWOVeKaBx4F\nc66UexLsVPx8kKwzp5TSjZH62INjXc2q+0b0TPeSGY8ps5Wbzeae2VxdkfSlcmOM+nTU8yYNPLoe\nTpckb+FjD5wzOx0jv0eaWPeic+bY0qXoHoNJQipFij3qIQ08PlmPwSyjtQNrztOZxdzKm3KHfZ+W\nwUBiPeqxFAaSqG1JnITQ1Bc5KJDO/k4A78t+/T4Ab7jI378LwB9Za4fC/++hAb0GONpborkD2G2n\n9MNY9JhZ652+QtA8U5zKKGdapDTYWGaXVeu9jNrxcdL8wItnJeeNA4+pzPI6F09WS7KcPCc1nq0i\ny5PWywdZybE48NiuOBuSF0AWMRQl+heuiVhe5IlpeXPS8t6W6x7vMhLq3oSge76cdIwCtt6C55FZ\n/XbEPdeibBxCdjwYozhxwrRTAetqPreYJLLXtKpjlDv6UbsiYUQv2U7J7EqModg88Bh71IPSZy7G\nDqb6dBIfMcbGIaxzjP23V3qu1bBxjGlellM5Ywn9gKh3gShDka97APEuQJA3D8o9x4RgSoxpJrmj\nxXVPsG9iPh35zneY+x8B8gDSCWvtl7NffwXAiYv8/TcD+O3gZ//BGPP3xpj3GGNWhOM5cKDXANdk\nl5oqQt5bIkbhbPxqWhiBn2M2l3XkByJMEukrBLHsEinrwGJxlbIOFBZEeEFsfoTUz5mY5RQ4G7U0\neeY3pDCQuM4GEGZaZA1jgeqcJYHHegYSs++T7NlcgOeksjPFTiZbXmWMgkDAk/G6p7hXRawMkNBn\nIWRJNdW9TruFdsvUsK5k2WwHaTClloFEZhhIx7h0OyUIPNYyZQWXG7ZdCeecluUIWFd0BtIC2yxi\nPPKSO712Wu45Y/V9qmPjdJoFHoGqLV0OW489Zy6LSxr8CHuuidk9NX4ntxH5TBR4DG3pSBhspfeR\nqmHzagDpIjDG/Ikx5lORf+70/55Nb6q2RgyMMScBPBfAh70f/xiAWwC8AMBxAD+64L9/hzHmHmPM\nPY888sjFhn1gEH8KVJ5pYR2g6RjJ7JkuN5gSn7M86xANmpH7LEhYENUmk/LmpCw2jpPJbJwXZorF\ngcfIOsuYKfX9OSSv2zCbndbV9DMZj5Ja9HSM5WyV/NXHuldPZN8wdDaaOkOdlknLPWdVB4sVNGMF\nHv0mk5LAo/tWodPbbRu0JYxH5suZ0WbIXDaORPecTGqZcMC6YugeUPVHJHYUYLP1Qjs1L/2/msrz\nH/VgMFvDRz1EZ2w3PGMZTJJq4LE5A6k++MGUB0hLH4llxzW+u1RXqpdimW1mJ7TCRuQpc43HUGQ0\nV+bOOdAVQjuLyhjpL5IJA491dz5mX8sl3PkkZ+xBQOdif8Fa+6q6PzPGPGyMOWmt/XIWIDq7QNSb\nAPy+tXbqyXbspbEx5tcA/PCCcbwXwHsB4MyZM7WBqoOGXqeVN5nstIvDT1rrXbkgNlTUdIxx50CS\n5WQGU2I9kMbJHOuDbiN5i5sPsjK7nKzDOJnnLz9Rsg6BYZM4G2GTSXZvHEbJSyqn7AQ21RXXj4L6\nVHC7ha3dpCKP3U+D2nNNEExxY6S+2BEEHpPZHIkg8NiLBAIkgce8t0TUqRTMmVhqEJ+znIHE0j03\nxlJ2XFrC1i6eEneOM6N/z3hW/oZSXaEGU8KX7EgMpHCdr15rRkavu9BJAo9VO0Vi683m6LfS78bo\ngRTaUioDifySHYP9B1Ttylr/oleaS5IHNPfpVjotbI992yzUFe9MHPQKP56te6J9U2enSI96iO8W\ndX5sw3246FEPFutK3ouLe+eLsXEkZYVA9c7H2DfMwOMydOUgQBo++xCAu7Nf3w3ggwv+7lsQlK9l\nQSeY1Pt6A4BPCcdz4BAyAqy1oLz0UnlZSspA4rzk4MYYvWSLX/ThRI9jvSXcS1WSCHws4yd+uWka\nrLNUXuCYN12TVCZ5nWupv0w2TvOMX/HaBC/wWM/G4fVFEL2KFO251vwLrZTkAAAgAElEQVSS7cYY\nCzyKXn1MinJPyVPGqbxqfw4K68rXZfErjXzdA3hnbPzMbq57bozxM7bp3i5n8F3gkfm6jXzfhIFC\n4XnTJcvL/rtRcEmUv4oU6p7ETj0JZ+wSXmGTMZDigUfWy1Jy9l/dGduQ5ZmVe4byOi2TJ24veYw1\ngUfmmTgW+XT1fmxThMwP+evEITNfLg+I+DfUCgyGP8K8C8TOREavPuIZ2+Wv83RWlHtKX62Ns4Nl\nQbODAOnsfxbAq40x9wJ4VfZ7GGPOGGN+2f0lY8yNAK4H8GfBf/+bxphPAvgkgKsA/LRwPAcOLtOw\nmylUQf1tpliD7L/bnYR9Fpor/6DXzhXeyQOaR48H3XZpfNIn7fuxOQvYOK1WGggYBQeoJBo96Lbz\nNXbjA5o7WPk6ZzJd4LGpvH4gD5AfoINeec5jYcDHfcM8ECDMcvZ7sTnLWBCDXhuj0j6UBR7Dbyh1\n9KO6Igj4uH04IumekxmuCdBcV/rdNqwtzlapvHzOFV1hzlnmEFX3jYxVEZ9z83WO2SnJc/FANucJ\nb879fG9n+0aYHXe2flQ6E4VnLNmu9Jdkp1j7pttuodMyVDuVzjnC4mq4F8N1ljIeQx/RyRafNzFd\nEYwxXGNAft7sTngsiHTO4QWx+Tfsd+vmTPTdBbY09BHdGKVzjtkpyTpPslYELHlA9f4jtivBN2y3\nTM78bzLGUdRO8c4HiS0t5PGCKYNuq+IjAhJb6hITs9K/qbqiTbQvXsK2CNbaxwB8XeTn9wB4u/f7\nfwJwOvL37pD8/w8Dwgud9GWIfsRhGyczrPaab4VaZ0OgrC4QYIwRZ6vqL3TM4AfX0R8LHbZ+4KS6\nLLnUCIWGTRRMqQQKhXu7VwQCUudNFkyJBT8oTmqwzmKHreREcy90ebNTouEV60qvXSoNkPbT8Ofc\n9zJhzAud5AUQJ9N32FzmuWngsR84qcuYs+SMjQUCpLrX77bx6PbEGx/PqdxAV9wwts5JPX6k10ie\nG0uoe4Ag8Fi5FHOCZsx1DgMBYyFTNgx+SEsfw0ustCwnGmwl6Eq4Ju7nTcc4nVlMZ2lJPUMeUL0U\nM4MfkrJjN8aY7onXOZM5m1tMZ9zAo7Rktno+SAOPrXyMV6x0Ct0jyHOQMqJjtlTyvHtdUpCajBH4\ndM52sAPWvm2W3nN9XTmy0hGfsbEE+ng6x5VHDncA6XDzr/YBVoNAgDQy6+QNQwYSMZgySoQR+CyY\n5Q5O6aXYBcfCA0/iVK522+VvKGVVVLLjsgNvtVteZzFjoRvbN9I5dzBkOhuBoZReEFeXlNll6t5q\nDfuv6To7XXFjTF/ya/4NO+0Weu1W5XwQXejCwCN5naWlj0s5YyNBesk+XO21MYyw9ZpeHvJvOPEY\nj4QzkXnerPba2J14PUmEgcdinZOSPGkwpTJniZ0K7UoyQ6/T/AXE1V4RCHDjA3hntpMpmXO/18bu\n1AswS8/Yiu7JSh/D82GvMQzcGGNJweZ2JfBjhfIGge4BEL246sYY2lLpNwx9RIC3zvIG9i0YE9M9\n2TekBj9yfyShyqPbUqpfXJUHEIL0kyLwOJk11xVjTOaPlHVPEnhc7XXorHIgchcQntmj0I/VEjbF\nXkZ4cZc2T42WIiUzcdahfKETRuC75Qh3QRnnBT8kEXjAOanLZOOQnI1838jktVtpI19upjigrU7n\naJmi2fSlIgwEsLKcbOcgNELygFSSBwLk+yaue5K+CP1uq1oyywxYE8orAN6Frr4MUMpAKrOupAEp\nl8FOx8fJ0LlvOJ1ZUeDRjZGtK1GWp3SdyTT5EfOMjTFlRbocP2Mbl9QHZ3Zaas0IFLIvdJGHCkjZ\nbPHjEe0WWpVAAL/0URJ4rL3QSRnRZB+sHJASMlPqgimkYKvUvykCAUQWVxD8yNtPCH33vExYGkyJ\nMUnIrHJGufo4KV5pZLP1pD4iUA0USl5cdWMpJzqECfSANCAPPEbukMLWLwcBGkDa4wiVX5rFiAYC\npD2QIs6G1GEDeI55P7gUz+cWE4LRCIMfDEe/YARwShVZBygQpyeLA48Rdo+kLAfwDGUeWGjo6Eez\nDrJGvtE5C8sr5rYoUWQHP6SXbDdGJsOgysaRBR6rl5tllMzye5JIyzUAnlMZzlnaMBaIU/nFZTlL\nKNursiqayXMvhVGDH+R9E5Yy83rZFIHHuW0uz8lk+iN+OTQgZ+tVdEV4xuaBAOacyYHHqh/LLdtj\nBB77kTJhqe5NEr9/D5dJIpXnxkjtaxnxiyWBx8q+IQXpmT5YrC+c1Ed0ctz4AF55uTQhD1RbJ0hf\nfRz0WpU1AZrb0jwRStIV9+1ZvUEPCg737PcB6oIpEsc8lqGTHHgx2qrs4uBopmXaavNSg6wkLsjc\nUOnJQsq4CwSEjXyllPH8ACVc6FYrmVj5OocUb5G8MBCQN1/nZPxc4FG6zmFpE8XZyDN0MuegyNwE\nuifaN1V6MjsgtdJpHngM6cnSl/F6nbR/zzDIqMmDKcGLZMI1AaoOlrT0sVoGKGUE8M7Yqp2awRAY\nj5UyYUFpQKU0mnDGVkq3hbYeqLL1pOVcYYJMss598jdc7aWNfJOZK6nnZMer35C7zqLSx0jgUVai\nUufHyvaNK4F3gUfROkeCH9J9A/DnzJIHRGypsLw8dsYyvmFYJtxUptPZim2W3n+Ijz1UzljhI0Ih\nk55imytlwtJ17lSSgi6h0gQhI1qqK62WQb/LfTjpIEADSHsc1awDh0lC7zlAzpoCxQsb0gi8u0xX\nS1R4TSYZzBR/bLTGeaTMjZNZytARSkqqjc0lhreOttq8kW+3bXInVXpxAPi6l1Nr84APK2jmXpaS\n7xv2hS6ebZfrXuiwscql8sCj+MwOyytkGT+ASPEOvuGYkOWMvdwkXeewf09fEHislJQI7RRQUxot\n2jeR8lEh+w/wWZ7c0keGba72siH5I54tNSYtHWskr65MWJrBZyYmovtQVs4F8NbZ2Y8R88zutXM7\n6sbI8WODxCXphVSpf+PGyFznMOgv9RHZZcKOrefkTLMX3uQPXPBYXLXrTGbKSs9YNiN6dzrLy/bk\nvf9cH10O4xGIJLSEScGDgMM9+32A+sbAxNchKK+ehM4L0UkV1gCHjXxZF8RSrwq2k5rMRBH4kLnG\nCH5U6pSll5GMtlr075HTYAFe4zygPGfpCyDpGMMXfTiBx0pfBGHPgeoFUXaJrc5ZpivJvAgEjAnP\nLQO8SzGQXpjcnIsXEPdQ6WNl38jOxD752Vw3xsrLMYRgqx+kpzj65ACzG597AZFZJky/0An3jWvk\nOwp0jztn4TpHbKn0BUTAZ0QT7EpFV7ilj2Ldq8xZxgIPy/YYZ3Z0zgRd8Zmtxsj7AVXYemImCXfO\nfv8eRmuHVA7xjPWCHww7VS2N5iQFK6XR7FcfmWV7hNJHf2wMuwdE2lmQ7EriAo/KQFLsZdQHAji0\n1WQ2RyJUBEdb9Rv5MoxG8fICIWjmXegYzkZYwiYPflQz+AwjxDSUq5VGvrJ1Xu118hch3BhlzZqD\nb0jQFT+bzWL/sZtyA+UMXadl0GmcHXf7xlHGGdmqcnmF9NncStNroe7VvnRJKnFlBDLDUgNx4LGm\nVLFp4LHXbpX697B0r1ruyTsTpZfsSpkw6Yx1do9zWaqWBkh1GfBZV/JGvn65FKPBa9jIl1HOBaDU\nyJfBlM3XmRH88M7YtB+Q/CnxcdC/h6IrxAtd7IyVng9hryuGrvh2ShJ4DBnW0gd1AL4/ktvSxDtj\nmeVcjLI9b86MMzbWwkMqDyj7sYzAI+uRECeTGbCOnQ9cVjkp8EhkNB0EHO7Z7wMsm4HEUIR+t9y/\nh9GUG4hc6MQX9+BSTKWtyl4hiPXvoUTgSTRYoHyJdS84UTJ0ZMecSdX1nQ1WIJOZ8Yuts0Re3siX\nyCSplLCRmuz76yztLwRUe9mwHCyKvKiTytg3he5JAo+OEcAuE66yPImNfFnfkGin0sxuYUcB+QXR\n79/DeMkOiLE8Zfo3nIZ2itzLhpHc8cqEOWWAnIcPUpmtSvCW4T+w2HrVSyyJEZ0nVrk+IpDpipBh\nDZSTO5LxrYStGAjJHT+xygo8lsZIs1NEJkkpKUgIPFaY+eQHLjL/pmngsdUyWOm0SroMNGc0AXVB\nM/k6+3c0dmIV4PULY5zZBwEaQNrjqOtlI1YEcqmBL0vck6Tm1QBWE1rGha5fcVL5l2LJN+y200a+\n1XWWOmyc51SBeLkU+4KYjpFD1V1G6aM48BhpQiv5hsXTvmFTbo7DlgceieVX7F4V7B5pLF3x+/fQ\nS2aFDiAQzpkUhPN1hRX8IAceWT3X3Bi5fV2y0sKk0GeZ7xBcihNZ4BEo21KKPxJ7YppYJiwNprg9\nx7Yr1LLjqF3hXYpZ61xhB5NffZQEFqq9bGQBqbCRL60shxh4jM6ZUCZcZYGTEuhEO5W/oLwEOyW1\nzWX2DPe8sdZmwVZ5mTAtYB28FsrWFQar/CBAA0h7HNX+PezsuFwRYq9DMOiMQ0/5JRF4ID5naQR+\n6BsNNm2VcKHzX/yilbBVygoZ60zKOtRQdZs2O3VjZDsvlUa+5OCH5IIIOGeDVz66Gjtv6LqyjECA\nVFd4DlulzI4UeCzrnswdWI1kdlllgMlsLg88kksfXf8eti0NG+JLS5sAv1xKmtwJX++T26nVHv9C\ntwzGI8u/abVckD4oHyWVCVOYKZGgGeNlKT/J2G4ZdBr2eHQy2WU5o+m83MiXUe7plQnLz9gOndla\nefiAbaeIdwFW4LHSHoPwavTIS1Azy4SlvUYBlF73pD32kMmbzOawVhZ4XM3Pm/IDF02xDMajXybM\nYHEdBBzu2e8T9D168s44PfiuWOk0ludHo3fG6b+PCOUBhbLujGcieWEWY3uciObrxugMOeUbdts5\nm2I2t9idkuY8LeYskefGOJqW941onbuRfdPbO3MOWVw74wRHem20BE6q72Dl31Aw5/AyItWVqjyC\nrngBH8q+8Rw2hrzYvpHMudduoeUFAnbGCdoZ7bspyrpCnPNkibpC2Df5Ok/cnIVOqhvfxNkpAlN2\nwtGVgq1HXGfPMd8mndkA8jJhqa6E2fF03wgD1t7lppiz7ILoGvmOkxmmM8uZs1vnCUFXevzzIdw3\nUh8RCO0KL3i7M57hSK/5C4hAfM6UMzFJE4PS8yEsE+adsURd8XzE7YnbN7yLe7rORFs/SdDrtNAV\nJAX914Q5d4EyozD1O3nnzfZEfsb6tnQ7890lc14t2T25vDChJdWVdsug12mVfESgCM41GiP5Hn4Q\noAGkfQC/9nI4TmAMr6+Lc/RXJY5+zMHq8VgVw8lMND4nsxhf+m/JYeLP2R16TKMxFH5DIAh+5HOW\nrXO4b6SOPuDNOXMqm8L17ykusTOs7sFgCpDOeZLMMZnN96SuhPtG6gS6jA0zEODrikSXwxd9diYJ\nVqWXm17VwaKsc8Z6HE5mlG/oO/qSbwg4J9U55oTERLeNSdbINz9jqYmORHQeujEOvSBXt506rhJ5\nvi4DHLviWE3DiWydK/tmIrsgAuXSR6otnc4wHMvtXizoT9k3wfngsvDNxlhcloo589Z5OElEttSx\nCXxbygimFPuGa1fGyRxzy/qGRVBdritFCRtLV/I1plyyA12ZJCJ/JA8EkHxEN0bH/nNzl+xtP/gx\nn9vMB5MkEbJeV9NiXSi6MinvG8m6uL51LtAKyO8WAEoJI0ZioljnBINuu/Gr1rk8b18DMl05CNAA\n0j6AT0/eziL6onKuXtFzYEhgkoS01Z2xzNnodcr9e6QRfaDcA2mHkLnxX8TIHUCSPCBdZ+nhFLIg\npKwKv0Rlh+BshKWP6YWOwQjwMn7iIFynmPNEbih9J3WX4LyE33CbpiuBUyna20UjX86+KesKxdnw\nzljGebMauRSzdGWcpC9nMnWPEbD2mxcznEp/jDuEQEDlvCExSUYTzvnlxljJmgp1D0jPG2ttNmeJ\no1/NtksD1lG7wkhMTGdFooOY3GH5I34gYKXTEvWR8kubcmYKI2gWMIaaIuzf44L0Evhlwmx2MIsJ\nB8BbZ3lyJ7wLAMX+bCav6N9DSe5EfDqxLe2FyR3+GSvTleLxFhf0kcjLH4MpJXd4urJN0JV+tw2b\nPZy0Q0juRO+QzPsP3UeU68pBgAaQ9gHCDB0lMhseoIzMzTRlVUgp4+EYKY4+u5zLaybqDlAmTX44\nkVHG0zG2S86LlDLu9+8pnA3OnN3lRrpvqroi3TetitFglYAwKONh5mY45mRufFo7gzIOpD1nOCW4\nXMq4k5k7bBRng0sZ73sXOpc1lchz/Xv84Ae1LIcQNPPPB8a+WZau+OcD0+4NGfvGm3N6UZSd2S5I\nP6LqXpUdTJnzZAZmiX7JlhIZAYwSfWf3UnYisfRxOqOU6DuZy9IVVok+kAUCCInVMJjCakvgVyNI\nS/SdXRl7tpkx57It5bLAxbrSK5KMTF3ZnczzhBtFnmdLqX7xeIYWoYoFSHWFxTYGyK0YAtIA5Q4Z\nsLikMvc7NIC0DxBGjxkR/WRuMUnmHBqsVxrAyDw7mbvkCHxRX0ugjHfT7zWcJBRWRSxzIy2/8ptM\nUpyXEiOAWMI2naXNK60s8wyUG31zdKVT6YHEoTvPKJTxlU65fw/rfCho7QTKuEfxprC48iaTHMo4\nAKx2y+vM0JVh4GyImCSRYIrkG7pAwJAcNPN7GIgp414zUUZAKmxOytYVSklcr2jkS2VxTWaUbLuT\nWSrLETvmZd0zRs6qAMrJHQaLq2xLGbrCZKa4C92ckhR0ujIkleg7mWVd4QceGboynJAYSGEZIMmW\nDr3AgtzuVX13EZOkW+yb6WyOSSIr0QfKc6boStf3EXkVGMNJQtk3rmyPqSuxO6Qkmezryg7Brvjy\nHBuOUSa86935GN/Qle0xWFwHARpA2gcIa72lDpuflWRnbhh0RqDaZJISgfcudP2ujDLuZyUZdEbX\nv8cPmjGbIVPYOF52iZKt8i/FBBaXk1nWFZkR8jM3uZMqCjwW+4bR7DRs5MtY536ge4zMDZA28qWy\nuCYFZVzK1vPnPBQ2/nRjdP172E1tl6ErFCZJ0OuKtW/8EjYW+y+ZzTFO5hRbymbKAmkjXwqz1ds3\nQ0KmGHC2NMvgCx8BSMfolTYxSvTZzLVO8Q0BFrPVY3mSdM+NsegHxNEVBsMAyB6DKZWUcM+vDuHh\nAyDoa0ls5MtmQVB8xFi5J6F/D/O88efMe1AnY+sRSmb7Md2j2NLl6ArzLpDOmfN4BFD0H5vN7RLu\nkPJApns4aaglbAA0gLQvEL42wchyAi7gQ6x79injjPpVP0NH6TlQvERDc9gmHOqv38jXUcal69wv\nOamc5oMAP/Doy2M28t0msLj8QIBjGEhfdQPKpUgUtp7f4JVRwjbxLsUE3QOCdabsmzmFxZXKLL/Y\nwShtAopyiJYB53JD+oZAEPwgN7WlvM7lX0YITBL3/dOANU/3RtQ5x5hmnGAr4yUaN8Zy8INZBshp\nbA7weme1ssBE2n8sfdWNOWdWQArg2dJ+pESfsbfLJbMcu+ceFWA8fACUeyAx+rCM/P5jxP6EDF2J\nlntK2MF+goy1b4LyK8ac5zZ9fp5aol/qncUt22OwuJg+Yr9kVwjlnqWSOA5TNkysiu8WXf75sN+h\nAaR9AJ8yPiTUw7oDeGec5K+6iSjjGW11Z8J1NvwXv6TR49Vu2sh3OptTWFz5N/TKcjhlL0m+1ox1\ndmNjvORQ7Btu4HE48RkG8nV2QYXheIYrCLTVdIwJxdEv7RuWs5HpinvVjTHnHS94y7oUp7qSrbOo\ngWPav2foyxOvS6d0xrLPhyMr8ocPgHRP03Sl18ZwzHnVzclze1r6fDPglQmPEwoDqdVyZXvkM9ad\nNxMOc83J2pnM0Gu3ZK+6+SWzNF3x7AojO541J53POX3wBsGZDXDmPBzzWBXpvvFYV6w5e7rCaOQ7\nHCeUfkBA6ifuEM/YQS9t5Duaziks9dWl6UrxqhvLRwQ4jMeiVLHQFVkz5OwuMJ5R+gEB6Zx3/IQW\na50zfebdpzhsHCdzxyvRp5QJj4t9w9UV+Z2v226h2zbYmXAYlEB4h+Sts9MVaYn+QYAGkPYB1vod\nXBh5tZxCRVhb6QIALowSyqtuV/Q7mbwp7VK81u/iwmgKgNMPaC0fY0JhcfnyWB351/odbHnypNHt\n9BsW/YAY8oBinTstg54gc7PSaaHXbmFrNKU9i7k+8HVF/kJQed/IWVzr/UL3WIHHtZVUVwqWhvAb\nZrrnar2l41sv6Uo6xoHgOxpjsLbSKclj6HOhK3LG4/rAW2cC49Hp3pZ3xsr1uYML4ynlVTcnb5zM\nMU5mFIaB072tJawzi8WV6p7Xc41g94BinXnnl7dvCDIvjBKPVSE9EzuwNm3Yz5hzqHsAy5ZOab0v\n1gdl/0b6DUu6QnjV7UivjZYp/CWAt28ADnNt3fdHCP2AfP9mm3TerIf7huQjAvKX8Zw8oPBvAFky\nud0yWO21ubrSD3WFtc4JhcXln7HbhOCtk3lhlBSvuhH2zc5klr+Ey9jXAIK9TT5jKX6sdxcg68ph\nL18DNIC0L7Ax6GIru9AxnI2N1cJJHRKeU2230gvd1q4f/BCOcdDF1ijJX3WjzXl3Ssl+bQzK8gC5\nk7ox6GJrd0pjcW0MunkzQwbDIJ/zKKFQxo0xWB90s33DmfN639MVAmW8mPMUQ0IQbn3gXxA5zsaG\n+4YTjrz1QQdzm2ZtGJRxX1d2JomYVZGOMdMVkrPhdA/gPGnvHKx0znLK+NpKB8a4CyKHEeD2zZC0\nbza8izuDMu7rHivA7GwpTfdWU3kA8jNROj4AuS2VrvFKp41+t5UHFgDWvpnmr7qJA9aDQFcIF05f\nHsCxpc7uAfJgynq/gwtj7xEApq4QApm5bfYSE5R9M5piNrcYTed0W8o6v7Z2Exp7Zn3QyeRxkoIb\ngy4myTzvNyrVvZIfSyjRdzLTfcO5CzgfEQDFloZ2hREMTuUlXk8luUzncwKcfQOkgVuGXVkv+cXy\nEn03xtQfIQVvB53cp2OcieGd77CXrwEaQNoXWB90MZ3ZvEkbIzILIL90Sql9boybXjBFKnO93ykF\nZ1hz3tydUijjbn7My0ga/OBRxn3mByNoVnLMCZRxIDvkPWdD7rCllxsWZdwZys3hlEL9zVlXuxzK\neDrGTqp7xMwNUAR8mLrHeDrdyaTum0z3ZiTK+PqgYAQMCbrSyoP0UwplPB1jFz6jiaYr2cVdbANK\nDtuMQhl3dorF/lvvdzCaFqwr+ZwLu5Ke2SRdyXQP4AXpWc2VS+cD4Yz12TiMEn2gOGNp+2bQLVhX\nhBL9UFeY+2aHaJs3h1wfEQA2dzn+jWNdbRIDj7mu5HPmJSYYJfqlIBzB7rkxbhKTO85HdK+6ieec\n7xuOrnTbLaz22iVdYaxLSR7NlrLuAp1cHqNE38l09zOAcT50cWHs+3Qc3WPdIQ8CNIC0D1DauIzs\n+KA4QBmUccBRa3nlFc7RZ1LGAS9DJ/yGnXYLV6wUTqWUMu7GuOXNmUXld9+Rum8I2S+gcFKZdOed\nyQybWeaBZTRcaaF0TdLMbidbEzll3I1xi0iTLwUCCJTxUPcYmZviG3IaLq4POpjNLR7dHqfyyM6G\nNJuWjjHQFYKDxQ2mlAPMYsq4Y12584tyKS4zZVl7+9xOGrRmsWfc3uboCt+Wli6IJLvi1oXRJHel\n08rPWGmJPuAFU4h2CsgSE4QSfV/3GI+EAF7QjMYOTllXbP9mK1tnqa74rCtGib4bo/8NuawrQrln\nv3wXkOpyOsZOHlgAOHZlksxxbmcCgMt4ZJQduzGW1pngP2zu8kq3/XVmlF+xfUQnk32HBIDHtseU\nEn32HfIgQANI+wDOwXro/C6sLTZyY3len4Wt3Wn+e9kYUwfL1We7MUvGmMwtHt4a0eQBqZO6NSLN\nObuMbO1OxWtSyJvmJRHSMbr//tHtMUbTuVjeWmnfJLlREo3R7Ztdzpw3sn3ywLkhRV5xuZny9k1m\nKLd2p1jrd8SU8Yruib9h4RxsjRKx7vmsq60RR1cc3TnXFdKZyNo3Pt2Zd950c93rtVtiyrijeG8O\nSeeNx7pinImtlsEVK51CHln3/DE3lhfuG6m80r5JeHZlVNiVNeG57VhXLtjKslO5P0I7H5zukRId\nme4BvHU+e2GE6czydY+gK65sj2WbHevqofO7FHl+KVK6b3gJLWenpIFHfx8CDDuVzvHccIoL40T8\nDY/0OmgZUG1zRVek5002pi/ltpkXCODZlU4ub9Bti151C+X5Y24uz/fp5HP2WVdbu3IfESjrXjpm\nTslZvm9IusfUlf0ODSDtAzhFuP+xVBGOrvZE8lZ7bXRaBlu7U5wbTnBMKM+NcXN3inM7E6z22ljp\ncOqUWXP2L8XnhlMcPcK7jKTfkOWwTXF+mGZajh0Rzjkb0xfdNxTK83tdMffN1ijB+eEULUO43FT2\nDS+Ycm6Hs87OUJ4nfsOdyQyPXkgvdFJdcc7FQ+d3MZtb8RhdZtfpHu0bjqY4nwU/NoTGnH3e+E4q\n+4w9P5zg6CrncjO3wIPZhY6lK49eGGNnMuOdibvpOkvH5+RtZnYP4NkVt2+k6+z3ujrPtCu76Rm7\n1u+ImbJuzl98PJuz0JbmZ+xwivO7pPPBs6XSNQZSW+rvG+k6u8tRsW9kc3a9rjaJuuLs1LnhFL3s\nwiiSR9YVn3XFtKW5j0j5hinr6rFtt284uvKlxzn7ptUynh875ehKcMaybbN0nX2fjmlXUr+Yc365\nXldfyRLorDk/vDXCJJlT1tn5YOdIZ2zqI6bfEGCcD9wzdn0J+2a/QwNI+wDOUOaXG+GB7FN1N3en\neaBBOsYLowTnd6fi8QGFg+WcVKmyOnn5ATrgHHj55YYkbzqz+PJmajSk39Htm/wbUtaluLhz9k3K\ngji/O8HGoCtn44RzFhohV4d9fjjB1ijBBslQbjldIX1DgK8rTp7EuJ0AABSjSURBVJ7UAXQyiwsd\nj/F4fjjBoNtGX9zjhBt49FlXtAtdVhrAvCACxH0TyKPoigsU7pKc1H5xZrdMGrARyQt1hXChc72u\nUrvCDqZw5AH++cAJtj6YBawptrTP1pWUdXU2C9JLz0S27jmZzpbS5I2m2NydYIMQsK74sSQ79ej2\nBDuTGc3v3Bol2CTqnvWD9CRdYSU6gGLfbA4nvPMmO78YAWu2f+P3uuL5YL6ucNYEYPp0SzhvMn9k\nk3jnc3eLbtvQAtbF/Ue2Lj7r6vxwQrFT+x0aQNoHyCmcVGejg03nYLGUf9cFFogHKCnrMOimrCvu\nN0wDAZtEIwSkBx7nAE0Nb5Gtkq/Lmgv4kA5QP/tFySCGrCvh3na9rh44lzqArOzShSzLyQnOBOcD\nKeP3xWU4qbu8LOfOZIZHt3mXJYB3xrog/dmtEcbJnBakz4MpJN0DeOvM3oepzIzxuMMLws2zMhpm\nwJp5xq4PulnAmmdLt3Ydu4dvm6Xr4lhXTNu8MfCz47wz9oHHh5SAdXjGSoNwQDm5w7Klue4Rk4Ks\ndXasK7pPt0sMWHvrbBgM6+C8odiVLGjGZl2xdY9lp5xt/vL5ES9gXdK9ZQTppQHr8C6wB8/YrNfV\n2a0Rjq72KAxrgGen3Bgf25nQGNb7HRpA2gfIqXjU6HEXD53fRTK3NMW6ME7w+M6YlsUAijlLD1Bn\nNO6nX26IlHGXzX5siI2B/ADNM37kfeMOUNa+mc4svrI5ol2yAfKc+x26vE2X8SNe6O5/fIi1FXnG\nz7Gu2PuGyqrwspIshhTgzZniVHbI8ro5fZqtK52WETeFdKwrrq6kc06znFxdYV4QqXal38UD59J+\nh0xWBS9gXdYVqUzX66rYNzxd2dydcoIzpX3D929YduXhrTExYN3B7nSGR7bH1CA9dZ37hU/HCrZu\nUu1UMWdGwHotvAuQxnguC1gzWVcPnNuln9kAz67czwzC9Tt5CS6VVf7YEP1uSxywdr2uijObrCtk\nW7oX75CpzA6VxbXfoQGkfYCNQRctA9z78AUAHMN7bLVHlXc8669z39ltcU8EADiejenehy9QMn5A\nGnWnznm1h0e2x3icVH+fz/nsNiW6nfaianlz5qyLk8cY4/Fsr9z78AXKNzzmyWuZwvmQyeTryrnh\nBI9cGFO+oeuVde/DFyi9vTrtFjYGXe46r3bxhUd3kBB6KgHlObPlAXtVV3oYTmZ48Pwuad8UusLI\n+BljcOyIv284uvLQ5i62xwlfVwjynAz2nO89u53KI+jzsdUeZnOLLzyyzdEVzzavrXTEDWOBbM5E\nXTm22sMjF8a0niTlfUNg8q500G0b/r4hymPP+bgnr9duUV4xou+bIz08vjPBY9uc5M5xsp3qd9s4\n0mvT1/nzZ7fTgDX5fKDY0cA2MwIBx5agK1ujBA9vjrj75iznzG61TOnOR7ErR3r40uNDjJM5zV8C\neLriywPkPWUB/r15v0MDSPsAnXYL16z1sTVK0Ou0csWQ4NTRfv5q06mNgVjeyY0+gLT550mCvBMb\nK4W8o32xPAA4dXRQzJkg8+TRASbJHJNkns9fKg9Ia7PdryUwxuDkRrrOLQOcWGeMsdg3jHV2MtJ9\nIx/fVUdW0G0bbI0SXLveF2f8gHSMXF0ZYG6BncmM8g3dXmbpHoB836S/JszZ0z2GPp866u0bgrz1\nfjd/8ev4kR4lYF2eM++MvUBa51Oe7jHOQ6CsK5QzcWOAC8RvWJoz4RuudNq46or08rDaa1Neozm5\n0cdm9voO53zg6krpzKbtmz5VV04d7WM4mWE2t6R9U5yxpwj7utUyOLGezrnTMrh6bUUsk617ZV2R\ny7tmbSV98WuU4NqNvjhgDfDt1KmjfSRzi93pjOMjlvxikq54duVayjr3yX6xryvyNTm22sVKp4Wt\nUYJr1lYoAetTpX3D05ULY46uuL28uTsl7huyrmyQ75C+H0vYh4NeG0dX0zYja/1OzqyXgH2H3O8Q\naaIx5luMMZ82xsyNMWcW/L3XGmM+Z4y5zxjzLu/nNxlj/jL7+e8YYzSkVwOnUKePDmiXYofTx3hO\nKpCOUYrUMV+hyQMKQ2EMz/A6nD62KpbnGwrenFM5J9b7JMPL3jf+N5TLa7VMvrYMeUB5jEwHC+CM\n8Zq1PtrZmXAdad84fT662sURhuEl721fV1hzdjJZusefM1f3jq520e+mZwJtztne7ndbeRZVgpKu\nML4hWfeAYl1OHx1wLsVkW8rWlUGvnbM9eLqSymm3DE6QgikOjDmX1oRlV7IxXrtRnN8S+Hv7OsIY\n2brSabfyJBbNv6HrCldeGijjyQMKfb7qihVOoqM0Z7kfy/YRjTG5P8I+s4G9qStXHumhl/nrjLsF\nUMx5baVDYXGxz8RTZN0DyraZI8/3Rzjrsp8hvVF+CsA3A/jzur9gjGkD+EUA3wDgVgBvMcbcmv3x\nuwG8x1r7dADnALxNOJ4DC3eAsqKefsCHEoH35J0iKevpo+QLXSZnvd/FSkdueMtzln/DfredN85m\nGDXA3zcsh41rKNkOmy+TvW/SJphyw8s2lO2WwVVXpJd12jpnZwIjswTwA8w+m44XKOSesf656tZH\ngtPkb2iMydeXHQg4ucEJprDnfKUX1GJkioFiv7DtHsBJdJxegm3Oz1iy7h0/0hP3cEvlcc+HK1bS\nHiKpbK4tZene6ZJPx7bNnMsS2x9xOjzotjEglMSVk0XyMXazcnCApytunU8v4S7AGKN/ZtHWmXzG\n+uvMCKaU7gIE3Wu1DK5Z5ybQnRwmazSULYHPwmSfD+z7FAAKa3S/Q2SprbWftdZ+7iJ/7YUA7rPW\n/qO1dgLg/QDuNKl3eQeA383+3vsAvEEynoOMW0+uAwCeetUVVHkAaP2FHJ51ck0sDwBuuuoIAOCZ\n13LkuTlfQ1L8m08Ua8FaF3cQP/MEac6n0jk/5TjHAfT3DaO/kM9uYa3zzdeka/EM8r45scYxvG5f\nA+U9JIEzbDRdyfYNy5A/y9s3DMPbbbfyDN0zr12/yN9+Yrgl+3Y3X8P6hhsA0ssnI5jiO0GsdXYX\nBva+ObHOOWNv8cbFCOwZY/Ig0i0nOfvG7e2nXc3RZV9XGKxRX99Y63zjVauZPM43dPuGZZuf4dnP\nG67k2L7rj7s5c+2Kbw8Y8gBQWKP+xZo156dd7Xw6kh+b7RvWZc7X4addw/XpbiGfsdeRmCnPPlXs\nG0YfKf8+wZqz0+dnsGxzpivHj8h7/wHADZ5//QyS707fN86PJbSy8OUBwLUEme2WwVp2brHmfEt2\nF2XpstO9bttQWKP7HXIrc3GcBvAl7/cPAHgRgCsBnLfWJt7PT9cJMca8A8A7AOApT3nKcka6h/Gt\nL7geD5zbxTu+5qkUec86uYZ3vvJpOHPDcYo8Ywx+6dufj394eBs3XMlxiL7v627GoNfBG59Xuy0u\nCV/7zKvxnS+9EXfefooib7XXwU/d+Wy0Wy1K9gsA3n3XV+FDn3gIX/OMqynyvvl5p/H5R7bx9pff\nRJF301VH8AOvurl0yZHiV+4+g0986TwtaPavvvZpmFvgTWeup8h76dOuxHe/7Ca89jnXUuT1Oi28\n+188F6PpnBKEA4CfuvM5+J2PfwmvvvUERd7rbzuFzzy0he986Y0UeaeODvAjr3kmbrhyleKwAcAv\n330G/+fzj+G26zYo8u5+6Y3YGiV460tuoMg7c8MxvONrnopXkHS51TJ4z7fehkcvTPLyXil+/Buf\nhd/4v/fjdc89SZH32udci7/94nm85YUcG33lFSv48dc9C1etcZgpAPAL3/Z8fPSzD+MFN3Js37e9\n8Cl4eGuM7375jRR5zz29gX/9iqfhxU+9kiLPGINf+Lbn4f7HhrRL5w+++pk4utrDN93GsaV33HIN\n7n7JDfjm519HkbfW7+InX38rBr02hW0MAD//ptvwP/7uy3j506+iyPuWM9fj/seH+B6ST/f0a67A\n993xdDz3uqMUeQDw3rd+NT790FYe+JHina98OtqtFu76ao5t/mc3pz7d62/jnF/9bhs/88bnYmYt\npWcKAPzMG5+L3/ubB/CKZ15DkXfnbafxua9cwHe9jOPTXX98FT/06mfg6ddcQbPNv/ZdL8DHv/B4\nKTglwdtefhNG0zne8iLOvnnRU6/E219+E15F8pc67RZ+7q6vwtYooTRrBoCfeP2z8Vt/dT9e82yO\n3/m6rzqJv3/wPL79xRz/5pr1Pt71Dbfg5Aan1ygA/NJbvxp/9g+P4PlPOUaR9x0vuRGP70xpfuzt\n1x3F93ztU2k2YL/DWGsX/wVj/gRAbAf/uLX2g9nf+d8Afthae0/kv78LwGuttW/Pfv9WpAGknwTw\nF1n5Gowx1wP4I2vtcy426DNnzth77qn8rxQKhUKhUCgUCoVCoVAoFA1hjPlra220x/VFQ+zW2lcJ\n//8PAvDDxtdlP3sMwFFjTCdjIbmfKxQKhUKhUCgUCoVCoVAo9hA4nPDF+DiAm7MX13oA3gzgQzal\nPn0MwF3Z37sbwAefhPEoFAqFQqFQKBQKhUKhUCguAaIAkjHmjcaYBwC8BMD/NMZ8OPv5KWPMHwJA\nxi76XgAfBvBZAB+w1n46E/GjAH7QGHMf0p5IvyIZj0KhUCgUCoVCoVAoFAqFgo+L9kDai9AeSAqF\nQqFQKBQKhUKhUCgUXCzqgfRklLApFAqFQqFQKBQKhUKhUCj2MTSApFAoFAqFQqFQKBQKhUKhWAgN\nICkUCoVCoVAoFAqFQqFQKBZCA0gKhUKhUCgUCoVCoVAoFIqF0ACSQqFQKBQKhUKhUCgUCoViITSA\npFAoFAqFQqFQKBQKhUKhWAgNICkUCoVCoVAoFAqFQqFQKBZCA0gKhUKhUCgUCoVCoVAoFIqF0ACS\nQqFQKBQKhUKhUCgUCoViIYy19nKP4ZJhjHkEwP2XexwkXAXg0cs9CIVij0P1RKF4YlBdUSieGFRX\nFIonBtUVheKJ4SDpyg3W2qtjf7AvA0gHCcaYe6y1Zy73OBSKvQzVE4XiiUF1RaF4YlBdUSieGFRX\nFIonhsOiK1rCplAoFAqFQqFQKBQKhUKhWAgNICkUCoVCoVAoFAqFQqFQKBZCA0iXH++93ANQKPYB\nVE8UiicG1RWF4olBdUWheGJQXVEonhgOha5oDySFQqFQKBQKhUKhUCgUCsVCKANJoVAoFAqFQqFQ\nKBQKhUKxEBpAukwwxrzWGPM5Y8x9xph3Xe7xKBRPNowxv2qMOWuM+ZT3s+PGmI8YY+7N/n0s+7kx\nxvznTF/+3hjzfO+/uTv7+/caY+6+HHNRKJYJY8z1xpiPGWM+Y4z5tDHm+7Ofq74oFB6MMX1jzF8Z\nY/4u05V/l/38JmPMX2Y68TvGmF7285Xs9/dlf36jJ+vHsp9/zhjzmsszI4VieTDGtI0xf2uM+YPs\n96onCkUAY8w/GWM+aYz5hDHmnuxnh9r/0gDSZYAxpg3gFwF8A4BbAbzFGHPr5R2VQvGk49cBvDb4\n2bsAfNRaezOAj2a/B1JduTn75x0A/iuQHuAAfgLAiwC8EMBPuENcoThASAD8kLX2VgAvBvDOzGao\nvigUZYwB3GGtvQ3A7QBea4x5MYB3A3iPtfbpAM4BeFv2998G4Fz28/dkfw+Zfr0ZwLOR2qn/kvlu\nCsVBwvcD+Kz3e9UThSKOV1prb7fWnsl+f6j9Lw0gXR68EMB91tp/tNZOALwfwJ2XeUwKxZMKa+2f\nA3g8+PGdAN6X/fp9AN7g/fy/2RR/AeCoMeYkgNcA+Ii19nFr7TkAH0E1KKVQ7GtYa79srf2b7NcX\nkDr8p6H6olCUkO357ey33ewfC+AOAL+b/TzUFadDvwvg64wxJvv5+621Y2vtFwDch9R3UygOBIwx\n1wH4RgC/nP3eQPVEoXiiONT+lwaQLg9OA/iS9/sHsp8pFIcdJ6y1X85+/RUAJ7Jf1+mM6pLiUCEr\nHXgegL+E6otCUUFWlvMJAGeROumfB3DeWptkf8Xf97lOZH++CeBKqK4oDj7+I4B/A2Ce/f5KqJ4o\nFDFYAH9sjPlrY8w7sp8dav+rc7kHoFAoFDFYa60xRp+JVCgyGGOuAPDfAfyAtXYrTQCnUH1RKFJY\na2cAbjfGHAXw+wBuucxDUij2FIwx/xzAWWvtXxtjXnG5x6NQ7HG83Fr7oDHmGgAfMcb8P/8PD6P/\npQyky4MHAVzv/f667GcKxWHHwxnVE9m/z2Y/r9MZ1SXFoYAxpos0ePSb1trfy36s+qJQ1MBaex7A\nxwC8BGkZgUua+vs+14nszzcAPAbVFcXBxssAfJMx5p+QttG4A8B/guqJQlGBtfbB7N9nkSYlXohD\n7n9pAOny4OMAbs5eO+ghbUD3ocs8JoViL+BDANzLBHcD+KD38+/IXjd4MYDNjDr6YQBfb4w5ljWj\n+/rsZwrFgUHWa+JXAHzWWvvz3h+pvigUHowxV2fMIxhjBgBejbRn2McA3JX9tVBXnA7dBeBPrbU2\n+/mbs9enbkLaEPWvnpxZKBTLhbX2x6y111lrb0R6B/lTa+2/hOqJQlGCMeaIMWbN/Rqp3/QpHHL/\nS0vYLgOstYkx5nuRbpw2gF+11n76Mg9LoXhSYYz5bQCvAHCVMeYBpK8T/CyADxhj3gbgfgBvyv76\nHwJ4HdIGjUMA3wUA1trHjTE/hTQoCwD/3lobNuZWKPY7XgbgrQA+mfV2AYB/C9UXhSLESQDvy16C\nagH4gLX2D4wxnwHwfmPMTwP4W6QBWWT//g1jzH1IH3V4MwBYaz9tjPkAgM8gfQXxnVlpnEJxkPGj\nUD1RKHycAPD7WcuADoDfstb+L2PMx3GI/S+TBpAVCoVCoVAoFAqFQqFQKBSKOLSETaFQKBQKhUKh\nUCgUCoVCsRAaQFIoFAqFQqFQKBQKhUKhUCyEBpAUCoVCoVAoFAqFQqFQKBQLoQEkhUKhUCgUCoVC\noVAoFArFQmgASaFQKBQKhUKhUCgUCoVCsRAaQFIoFAqFQqFQKBQKhUKhUCyEBpAUCoVCoVAoFAqF\nQqFQKBQLoQEkhUKhUCgUCoVCoVAoFArFQvx/FKrve72HZy8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Rb0gRcGUc9I",
        "colab_type": "code",
        "outputId": "71f206a5-68e6-494f-aedd-79329fad1cc8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        }
      },
      "source": [
        "# Plot first 1000 values\n",
        "plt.figure(figsize=(20,6))\n",
        "plt.plot(df.values[:100])\n",
        "plt.show()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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t219ar11p2Zo5soceHtBenh6sd4SLo0ACgBrgijbhWjOtv+oF+mjMoo1atvmo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\n",
            "text/plain": [
              "<Figure size 1440x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UDOkeQBwY_kw",
        "colab_type": "code",
        "outputId": "57cad8cc-99eb-41d8-bfd6-136183d71a43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131
        }
      },
      "source": [
        "# Normalize features \n",
        "scaler = MinMaxScaler(feature_range=(-1, 1))\n",
        "scaled = scaler.fit_transform(df.values)\n",
        "series = pd.DataFrame(scaled)\n",
        "print(series.head())\n",
        "print(series.shape)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "          0\n",
            "0  0.841483\n",
            "1  0.873749\n",
            "2  0.902566\n",
            "3  0.927822\n",
            "4  0.949416\n",
            "(5001, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aGZoRW-WVKcY",
        "colab_type": "code",
        "outputId": "47be8e0c-4740-4e40-93f1-6c21395b1cb6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        }
      },
      "source": [
        "# Setup the time steps\n",
        "window_size = 50\n",
        "\n",
        "series_s = series.copy()\n",
        "for i in range(window_size):\n",
        "    series = pd.concat([series, series_s.shift(-(i+1))], axis = 1)\n",
        "    \n",
        "series.dropna(axis=0, inplace=True)\n",
        "print(series.head())\n",
        "print(series.shape)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "          0         0         0  ...         0         0         0\n",
            "0  0.841483  0.873749  0.902566  ... -0.767129 -0.805896 -0.841483\n",
            "1  0.873749  0.902566  0.927822  ... -0.805896 -0.841483 -0.873749\n",
            "2  0.902566  0.927822  0.949416  ... -0.841483 -0.873749 -0.902566\n",
            "3  0.927822  0.949416  0.967263  ... -0.873749 -0.902566 -0.927822\n",
            "4  0.949416  0.967263  0.981292  ... -0.902566 -0.927822 -0.949416\n",
            "\n",
            "[5 rows x 51 columns]\n",
            "(4951, 51)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YNaRMCJjj5x6",
        "colab_type": "code",
        "outputId": "2ffee9fe-782d-4c81-9782-959df405ffe3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        }
      },
      "source": [
        "# Split data into training (80%) and test (20%)\n",
        "nrow = round(0.8*series.shape[0])\n",
        "train = series.iloc[:nrow, :]\n",
        "test = series.iloc[nrow:,:]\n",
        "print(train.shape)\n",
        "print(test.shape)\n",
        "\n",
        "from sklearn.utils import shuffle\n",
        "train = shuffle(train)\n",
        "# Discard the last value\n",
        "train_X = train.iloc[:,:-1]\n",
        "train_y = train.iloc[:,-1]\n",
        "test_X = test.iloc[:,:-1]\n",
        "test_y = test.iloc[:,-1]\n",
        "\n",
        "train_X = train_X.values\n",
        "print(train_X.shape)\n",
        "train_y = train_y.values\n",
        "print(train_y.shape)\n",
        "test_X = test_X.values\n",
        "print(test_X.shape)\n",
        "test_y = test_y.values\n",
        "print(test_y.shape)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3961, 51)\n",
            "(990, 51)\n",
            "(3961, 50)\n",
            "(3961,)\n",
            "(990, 50)\n",
            "(990,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oFjFFY90pnky",
        "colab_type": "text"
      },
      "source": [
        "**Note**: Preparing the 3D input vector for the LSTM. Remember, the input vector for LSTM is 3D array: (num_samples, num_time_steps, num_features). In this case we have num of time steps = 50 and num_features = 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RwicjlM8pUsg",
        "colab_type": "code",
        "outputId": "acd2fec1-5b4c-4546-d1b2-d68d4a9e1c55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "train_X = train_X.reshape(train_X.shape[0],train_X.shape[1],1)\n",
        "test_X = test_X.reshape(test_X.shape[0],test_X.shape[1],1)\n",
        "print(train_X.shape)\n",
        "print(test_X.shape)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3961, 50, 1)\n",
            "(990, 50, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X161-zWXpxpC",
        "colab_type": "code",
        "outputId": "4f1964de-02cd-4738-8545-df36a258f915",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 586
        }
      },
      "source": [
        "# Define the LSTM model\n",
        "model = Sequential()\n",
        "model.add(LSTM(input_shape = (50,1), output_dim= 50, return_sequences = True))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(LSTM(256))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(1))\n",
        "model.add(Activation(\"linear\"))\n",
        "model.compile(loss=\"mse\", optimizer=\"adam\")\n",
        "model.summary()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: UserWarning: Update your `LSTM` call to the Keras 2 API: `LSTM(input_shape=(50, 1), return_sequences=True, units=50)`\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3733: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_1 (LSTM)                (None, 50, 50)            10400     \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 50, 50)            0         \n",
            "_________________________________________________________________\n",
            "lstm_2 (LSTM)                (None, 256)               314368    \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 257       \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 1)                 0         \n",
            "=================================================================\n",
            "Total params: 325,025\n",
            "Trainable params: 325,025\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aNsBYFHqqE09",
        "colab_type": "code",
        "outputId": "ad2772d0-0dff-47bb-93df-997d585c6dfa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        }
      },
      "source": [
        "t1 = datetime.datetime.now()\n",
        "history = model.fit(train_X, train_y, batch_size=512, epochs=5, validation_split=0.1)\n",
        "t2 = datetime.datetime.now()\n",
        "print(\"Compilation Time : \", t2-t1)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 3564 samples, validate on 397 samples\n",
            "Epoch 1/5\n",
            "3564/3564 [==============================] - 1s 313us/step - loss: 0.0119 - val_loss: 2.7717e-04\n",
            "Epoch 2/5\n",
            "3564/3564 [==============================] - 1s 305us/step - loss: 0.0104 - val_loss: 5.0883e-04\n",
            "Epoch 3/5\n",
            "3564/3564 [==============================] - 1s 308us/step - loss: 0.0095 - val_loss: 8.3049e-04\n",
            "Epoch 4/5\n",
            "3564/3564 [==============================] - 1s 300us/step - loss: 0.0086 - val_loss: 4.8961e-04\n",
            "Epoch 5/5\n",
            "3564/3564 [==============================] - 1s 329us/step - loss: 0.0076 - val_loss: 3.9359e-04\n",
            "Compilation Time :  0:00:05.552616\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "72IBNoChs49t",
        "colab_type": "code",
        "outputId": "36a991c4-a292-4359-8aa4-e9cd162c290e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "# Doing a prediction on all the test data at once\n",
        "preds = model.predict(test_X)\n",
        "preds = scaler.inverse_transform(preds)\n",
        "print(preds.shape)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(990, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DM05CoJUwBWi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#test_y = test_y.reshape(-1, 1)\n",
        "#print(test_y.shape)\n",
        "actuals = scaler.inverse_transform(test_y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5s7a50bNxqyQ",
        "colab_type": "code",
        "outputId": "ad7e32b5-b053-4a15-ed50-49b46db053c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "rmse_error = mean_squared_error(actuals, preds)\n",
        "print(rmse_error)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.0003958811954194622\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2WWB4zfBzKkv",
        "colab_type": "code",
        "outputId": "efe3204d-d2cc-429e-cc16-187cae946fd8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        }
      },
      "source": [
        "plt.figure(figsize=(20,6))\n",
        "plt.plot(history.history['loss'], label='Train Loss')\n",
        "plt.plot(history.history['val_loss'], label='Test Loss')\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epochs')\n",
        "plt.legend(loc='upper right')\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 1440x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}